Improving Operations of Methanol Refining

University of Auckland Doctoral Thesis

Improving Operations of Methanol Refining

Isuru A. Udugama

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Chemical and Materials Engineering, 2016 The University of Auckland

February 22, 2017

Abstract Improving the operations of a high purity, multi-component industrial methanol distillation unit is a fine balancing act. On one hand, the operators need to ensure the ethanol impurities in distillate methanol stream are kept below the federal AA grade specification of 10 ppm. On the other hand, the operators need to maintain an adequate product recovery rate (β) of 97.5 %+ for an economically favourable operation. In current operations, with a lack of advanced controls present, the operators have no other choice than to use excess reboiler duty to offset effects of complex process dynamics and maintain these demanding specifications, limiting the levels of product recovery, reducing column throughput and/or stability. This thesis describes the author’s investigation into how better process control and operational changes can improve the product recovery, stability and energy efficiency of these columns. To achieve these objectives, a validated process simulation of an industrial methanol distillation column was built using commercially available software. A steady state analysis of the column confirmed that the column can be operated at a β of 99.5 %+, and that lowering the side draw location and reducing the side draw flow rate can improve energy efficiency. Analysis of the column dynamics showed that the ethanol profile forms a bulge near the side draw that needs to be explicitly managed. A novel, practical control scheme was developed to detect and manage the ethanol bulge movement. This control scheme was able to maintain on specification operations during process disturbances with normal levels of reboiler duty, while a standard DV control structure was unable to maintain specification. The proposed control scheme however, does not monitor or control β. To operate the column at a β of 99.5 %+, a new control structure that explicitly controls both β and product purity was necessary. A novel, practical control scheme based on override logic was developed for this purpose and its performance was compared with a model predictive control (MPC) setup. During disturbance tests, both controllers maintained on-specification product at β of 99.6 %, although the MPC was slightly more energy efficient. Further analysis showed a multitude of economic and practical factors that need to be considered in deciding between the two control schemes. To successfully implement in industry the controls suggested in this thesis, it is necessary to capture all the cost and benefits of these controls (operations). ii

Current controller performance assessment tools in literature are not capable of this type of explicit economic analysis. To overcome this gap, a framework was proposed that combines both layer of protection analysis and net present value assessment to explicitly calculate all costs and benefits of better control/operations. This framework was then applied to compare and demonstrate the superiority of the proposed 99.5 %+ β operations over current operations.

iii

Acknowledgements I would like to thank the following people and companies • Encouragement and continued support I received from my mother and farther. • My main supervisor Professor Brent R. Young for finding the right balance between challenging me and encouraging me, without your guidance I would have never completed this PhD. I also thank you for letting me attend many conferences and for slowly turning me into an academic. • Professor Rob Kirkpatrick for helping me develop a project which is both academically and industrially relevant, and for guiding me both in my personal life and professional career. • My co-supervisor Dr Wei Yu for countless hours spent on arguing the finer details of my work and teaching me how to better communicate my ideas to the academic public. • Michael A. Taube and S&D consulting LLC for helping in making ideas in this thesis more industrially applicable/acceptable. • My collogues at the industrial information and control centre (I 2 C 2 ) for your continued support, and for putting up with me for 3 years. • Methanex New Zealand Ltd. for providing access to a vast amount of plant data and for carrying out plant tests which provided invaluable information in validating process models. • The contribution from the Interns: Michael Kraller, Florian Wolfenstetter, Alexander Maidl, Philip Pfauser, and Corrina Zander as well at Part 4 students Ryan McSweeny, Abbey Robberts, Zhen Lim and Jeremy Hitchens for their help. • Virtual Materials Group (VMG) for support provided in developing process simulations. • The University of Auckland for providing me with a full Doctoral Scholarship.

v

Preface This thesis is comprised of an edited collection of papers that focus on improving the operations of methanol distillation with the aid of better process control and steady state operational changes. These papers are as follows: 1. Side draw optimisation of a high purity, multi-component distillation column. Kraller, M., Udugama, I., Kirkpatrick, R., Yu, W., Young, B. (Concept presented at Chemeca 2013, Brisbane, Australia. First revision of the full paper completed at Asia Pacific Journal of Chemical Engineering.) 2. Side Draw Control Design for a High Purity Multi-component Methanol Distillation Column. Udugama, I., Munir, T., Kirkpatrick, R., Yu, W., Young, B. (Concept presented at ADCONIP 2014, Hiroshima, Japan. First revision of the full paper completed at ISA Transactions.) 3. A Comparison of a Novel Robust Decentralised Control Strategy and MPC for Industrial High Purity, High Recovery, Multicomponent Distillation. Udugama, I., Wolfenstetter, F., Kirkpatrick, R., Yu, W., Young, B. (Concept presented at ESCAPE 2015, Copenhagen, Denmark. Full paper accepted for review at ISA Transactions.) 4. Comprehensive economic analysis of control schemes based on a layer of protection analysis (LOPA) inspired framework. Udugama, I., Taube, M., Kirkpatrick, R., Yu, W., Young, B. (Accepted for Presentation at Hazards Australasia 2016, Full paper to be submitted to a suitable journal.)

vi

Contents

Acknowledgements

v

1 Introduction & Background

1

1.1

Prologue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1.2

Methanol Production Process . . . . . . . . . . . . . . . . . . . . .

2

1.2.1

Reforming . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

1.2.2

Steam Loop . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

LPS upsets . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

1.2.3

Methanol Synthesis . . . . . . . . . . . . . . . . . . . . . . .

6

1.2.4

Methanol Distillation . . . . . . . . . . . . . . . . . . . . . .

7

1.2.5

Alternative Configurations . . . . . . . . . . . . . . . . . . .

8

1.3

1.4

Methanol Refining Column . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.1

Excess Reboiler Operating Strategy . . . . . . . . . . . . . . 15

1.3.2

Industrial Aims . . . . . . . . . . . . . . . . . . . . . . . . . 16

Control of Distillation Columns . . . . . . . . . . . . . . . . . . . . 17 1.4.1

Distillation Control Basics . . . . . . . . . . . . . . . . . . . 17

1.4.2

High Purity Methanol Distillation . . . . . . . . . . . . . . . 18 High Purity Methanol/Water Distillation Control . . . . . . 19

1.4.3

High Purity Distillation Control . . . . . . . . . . . . . . . . 20 vii

1.4.4 1.5

Columns with side draws . . . . . . . . . . . . . . . . . . . . 22

Common Practices in Distillation Control . . . . . . . . . . . . . . . 23 1.5.1

Process simulations . . . . . . . . . . . . . . . . . . . . . . . 24

1.5.2

APC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 MPC algorithms . . . . . . . . . . . . . . . . . . . . . . . . 27

1.6

Optimization of Distillation Columns . . . . . . . . . . . . . . . . . 29

1.7

Value of Better Process Control . . . . . . . . . . . . . . . . . . . . 31

1.8

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

1.9

Project Aim and Scope . . . . . . . . . . . . . . . . . . . . . . . . . 33

1.10 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2 Steady State Operational Changes

37

2.1

Prologue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.2

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.4

Distillation Columns With Side Draws . . . . . . . . . . . . . . . . 41

2.5

Model Description 2.5.1

. . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Additonal Industrial Validation . . . . . . . . . . . . . . . . 47

2.6

Increasing the Methanol Yield . . . . . . . . . . . . . . . . . . . . . 48

2.7

Optimisation Of The Side Draw Column . . . . . . . . . . . . . . . 51

2.8

Design Of Experiment . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.9

Column Sensitivity Towards Disturbances at Increased Recovery . . 54

2.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

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3 Side Draw Ethanol Bulge Control

57

3.1

Prologue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.2

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.4

Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.5

Dynamic Behaviours . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.6

3.7

3.5.1

Temperature & Composition Observations . . . . . . . . . . 63

3.5.2

Fusel Ethanol Dynamics . . . . . . . . . . . . . . . . . . . . 66

Side Draw Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.6.1

Ethanol Bulge Detection . . . . . . . . . . . . . . . . . . . . 71

3.6.2

Side Draw Controller . . . . . . . . . . . . . . . . . . . . . . 73

3.6.3

Use in Other Applications . . . . . . . . . . . . . . . . . . . 74

3.6.4

Distillation Composition Control . . . . . . . . . . . . . . . 75

Controller Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 75 3.7.1

Set-point Change . . . . . . . . . . . . . . . . . . . . . . . . 76

3.7.2

Disturbance Rejection . . . . . . . . . . . . . . . . . . . . . 77 Feed Flow Disturbance . . . . . . . . . . . . . . . . . . . . . 78 Feed Ethanol Concentration Disturbance . . . . . . . . . . . 79

3.8

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4 High Recovery Control Structures

83

4.1

Prologue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.2

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

ix

4.3

Introduction and Background . . . . . . . . . . . . . . . . . . . . . 84

4.4

Set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.5

Modelling & Validation . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.6

Control configurations . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.7

4.8

4.9

4.6.1

Process Dynamics . . . . . . . . . . . . . . . . . . . . . . . . 89

4.6.2

Proposed Control Scheme . . . . . . . . . . . . . . . . . . . 90

4.6.3

Model Predictive Control (MPC) . . . . . . . . . . . . . . . 96

Feed step tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.7.1

Feed flow rate step test . . . . . . . . . . . . . . . . . . . . . 99

4.7.2

Feed Methanol Content Step Tests . . . . . . . . . . . . . . 101

Disturbance Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.8.1

Disturbances in Feed Flow Rate . . . . . . . . . . . . . . . . 103

4.8.2

Disturbances in Feed Methanol Content . . . . . . . . . . . 106

4.8.3

Disturbances in Feed Ethanol Content . . . . . . . . . . . . 108

Energy Savings and Economic Benefits . . . . . . . . . . . . . . . . 110

4.10 Economic Implications of MPC vs the Proposed Control Scheme . . 111 4.10.1 Monetizing Energy Savings . . . . . . . . . . . . . . . . . . . 111 4.10.2 Cost of Implementation and Other Considerations . . . . . . 112 4.11 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5 Economic Performance Analysis

115

5.1

Prologue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5.2

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

x

5.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

5.4

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5.4.1

5.5

5.6

5.7

5.8

Initial Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.5.1

Day to Day Operations . . . . . . . . . . . . . . . . . . . . . 120

5.5.2

Major Process Disturbances . . . . . . . . . . . . . . . . . . 120

5.5.3

Cost of Advanced Control . . . . . . . . . . . . . . . . . . . 121

LOPA Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 5.6.1

Identify Consequences to Scenarios . . . . . . . . . . . . . . 123

5.6.2

Select a Single Scenario to Analyse Further . . . . . . . . . . 123

5.6.3

Identify the Initiating Event of Each Scenario and the Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

5.6.4

Identify Independent Layers of Protection and Quantify the Probability of Failure on Demand . . . . . . . . . . . . . . . 124

5.6.5

Estimate the Risk of the Scenario Happening by Combining Steps 5.6.3 & 5.6.4 . . . . . . . . . . . . . . . . . . . . . . . 125

5.6.6

Evaluating the Risks Concerning Scenarios . . . . . . . . . 126

Net Present Value Analysis

. . . . . . . . . . . . . . . . . . . . . . 127

5.7.1

Annual Profit . . . . . . . . . . . . . . . . . . . . . . . . . . 127

5.7.2

Discount the Annual Net Benefits . . . . . . . . . . . . . . . 128

5.7.3

Calculating NPV . . . . . . . . . . . . . . . . . . . . . . . . 128

Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 5.8.1

5.9

Case Study Background . . . . . . . . . . . . . . . . . . . . 118

Normal Operations . . . . . . . . . . . . . . . . . . . . . . . 129

LOPA of the Case Study . . . . . . . . . . . . . . . . . . . . . . . . 131 xi

5.9.1

Identifying an Initiating Cause . . . . . . . . . . . . . . . . . 131

5.9.2

Quantifying the Impact of an Initiating Cause . . . . . . . . 132

5.9.3

Probability of Propagation and the Economics Risks Associated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Current strategy . . . . . . . . . . . . . . . . . . . . . . . . 133 Proposed strategy . . . . . . . . . . . . . . . . . . . . . . . . 133 Proposed strategy with backup . . . . . . . . . . . . . . . . 134

5.10 Cost vs. Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 5.10.1 Proposed Control Scheme (without backup) versus. Current Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 5.10.2 Proposed Control Scheme (with backup) versus Current Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Annual change in profit

. . . . . . . . . . . . . . . . . . . . 138

5.10.3 Net Present Value Analysis . . . . . . . . . . . . . . . . . . 139 5.11 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 6 Conclusions

143

6.1

Operational Improvements . . . . . . . . . . . . . . . . . . . . . . . 143

6.2

Practical Robust Control . . . . . . . . . . . . . . . . . . . . . . . . 144

6.3

Value of Better Control . . . . . . . . . . . . . . . . . . . . . . . . . 146

6.4

Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

A Design of Experiments Steady State

149

B Establishing a method of calculating recovery

157

B.1 Prologue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 xii

B.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 B.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 B.4 Set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 B.5 Modelling & validation . . . . . . . . . . . . . . . . . . . . . . . . . 161 B.6 Basis of calculation and estimates . . . . . . . . . . . . . . . . . . . 162 B.6.1 Methanol recovery rate calculation . . . . . . . . . . . . . . 164 B.7 Sensitivity fusel stream density on recovery rate calculation . . . . . 166 B.8 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 B.8.1 Reboiler Duty . . . . . . . . . . . . . . . . . . . . . . . . . . 167 B.8.2 Feed Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 B.8.3 Methanol/ Water ratio . . . . . . . . . . . . . . . . . . . . . 169 B.8.4 Ethanol Content . . . . . . . . . . . . . . . . . . . . . . . . 170 B.8.5 Practical Implications . . . . . . . . . . . . . . . . . . . . . 171 B.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 C Design of Backup control for non-routine process upsets

173

C.1 Prolouge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 C.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 C.3 Introduction and Background . . . . . . . . . . . . . . . . . . . . . 174 C.4 Proposed Control strategy . . . . . . . . . . . . . . . . . . . . . . . 175 C.5 MPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 C.6 Disturbance testing . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 C.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

xiii

List of Figures 1.1

Simplified schematic of methanol production . . . . . . . . . . . . .

2

1.2

Reforming Proces . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

1.3

Methanol synthesis process . . . . . . . . . . . . . . . . . . . . . . .

7

1.4

Methanol distillation process, Picture courtesy of Methanex Corporation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

Alternative methanol refining configuration with 4 columns from [Thyssenkrupp (2017)] . . . . . . . . . . . . . . . . . . . . . . . . .

9

1.6

Alternative methanol refining unit with a 3 column configuration

9

1.7

Alternative methanol refining configuration with an extra recovery column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.8

Schematic of a typical methanol refining column [Kirkpatrick (2015)], (MeOH = methanol, EtOH = Ethanol) . . . . . . . . . . . . . . . . 11

1.9

Current control strategy . . . . . . . . . . . . . . . . . . . . . . . . 14

1.5

.

1.10 A totally condensing Binary Distillation with two product streams . 19 2.1

Different sections of a distillation column with side draw. . . . . . . 42

2.2

Distillation column as implemented in Aspen Plus. . . . . . . . . . 45

2.3

Methanol and ethanol concentration profiles in the column with different side draw positions. . . . . . . . . . . . . . . . . . . . . . . 50

2.4

Reboiler duties for different side draw locations for an operation with 99.5 % recovery. . . . . . . . . . . . . . . . . . . . . . . . . . . 51

xv

2.5

Reboiler duties for different side draw locations for an operation with 97.7 % recovery. . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.6

F-factor profile of the column at different operation points. . . . . . 53

2.7

Reboiler duties with operation at 99.5 % recovery for different column heights. The standard line indicates the current reboiler duty usage for comparison purposes . . . . . . . . . . . . . . . . . . . . . 53

3.1

Temperature profiles for different distillate ethanol concentrations . 64

3.2

Ethanol profiles for different feed conditions . . . . . . . . . . . . . 64

3.3

Column composition profiles for different feed compositions . . . . . 65

3.4

Responses from the different reboiler duty decrease step changes . . 67

3.5

Responses from the different reboiler duty increase step changes . . 68

3.6

Dynamic movement of the ethanol bulge . . . . . . . . . . . . . . . 69

3.7

Control diagram of the side draw controller(SDC) . . . . . . . . . . 70

3.8

Ethanol profiles at the side draw under different conditions . . . . . 71

3.9

Overall distillation composition control scheme with side draw controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.10 Set point tracking characteristics of the proposed control scheme when the product ethanol specification is changed . . . . . . . . . . 77 3.11 Response of the Side draw control and DV control to feed flow disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 3.12 Response of the Side draw control and DV control to feed ethanol disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.1

Process response of product ethanol specification for changes in product recovery ratio (β) . . . . . . . . . . . . . . . . . . . . . . . 89

4.2

Schematic of the proposed control scheme (including regulatory controllers) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 xvi

4.3

Control Diagram of the RC controller illustrating the inputs, outputs and the information flow within the controller. Where (1),(2),(3) and (4) refer to Equations 4.1 , 4.2 , 4.3 and 4.4 that are used in the RC controller calculations. . . . . . . . . . . . . . . . . . . . . . 94

4.4

Control diagram of DR contoller illustrating the inputs out puts and the information flow with in the controller. Where (5) refers to Equation 4.5 used to in the DR controller calculations . . . . . . 95

4.5

schematic of the high-purity methanol distillation column with model predictive control (MPC) . . . . . . . . . . . . . . . . . . . . . . . . 98

4.6

Reboiler duty response of MPC and proposed control scheme (RCDR) to changes in feed flow rate . . . . . . . . . . . . . . . . . . . . . . 99

4.7

Responses in β and XD of MPC and proposed control scheme (RCDR) to changes in feed flow rate . . . . . . . . . . . . . . . . . 100

4.8

Reboiler duty response of MPC and proposed control scheme (RCDR) to changes in feed methanol content . . . . . . . . . . . . . . . . . . 101

4.9

Responses in β and XD of MPC and proposed control scheme (RCDR) to changes in feed methanol content . . . . . . . . . . . . . 102

4.10 The effect of short time period and long time period feed flow rate fluctuations on reboiler duty, XD and β . . . . . . . . . . . . . . . . 104 4.11 The effect of short time period and long time period methanol concentration fluctuations on reboiler duty, XD and β . . . . . . . . . . 107 4.12 The effect of short time period and long time period period feed ethanol concentration fluctuations on reboiler duty, XD and β . . . 109 5.1

Simplified flow diagram of the high-purity methanol distillation column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

A.1 Q-Q plots of the effect for 97.7 % . . . . . . . . . . . . . . . . . . . 151 A.2 Q-Q plots of the effect for 99.5 %. . . . . . . . . . . . . . . . . . . . 152 A.3 Effects of the different disturbances on the response variables. . . . 154 xvii

B.1 Simulated model with controller used for testing purposes . . . . . . 162 B.2 Methanol mass fraction against density in an aqueous solution at 298.15 K . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 B.3 Recovery response for step changes in reboiler duty . . . . . . . . . 168 B.4 Recovery response for step changes in feed flow rate . . . . . . . . . 169 B.5 Recovery response for step changes in methanol/water fraction content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 B.6 Recovery response for step changes in ethanol content . . . . . . . . 171 C.1 A simplified control diagram of the proposed control structure . . . 176 C.2 A simplified MPC diagram . . . . . . . . . . . . . . . . . . . . . . . 177 C.3 Short term reboiler duty disturbance testing with a 50 minute sine wave. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 C.4 Disturbance testing 2000min. . . . . . . . . . . . . . . . . . . . . . 180

xviii

List of Tables 1.1

Average physical dimensions . . . . . . . . . . . . . . . . . . . . . . 14

1.2

A brief summery of each chapter

1.3

Thesis aims addressed by each chapter and novelty . . . . . . . . . 36

2.1

Mass fractions in feed. . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.2

Mass flow rates for column operating at 97.7 % recovery. . . . . . . 46

2.3

Deviation of the Aspen Plus model from the data provided by industry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.4

Methanol mass flow rates for column operating at 97.7 % recovery. . 49

2.5

Deviation of the Aspen Plus model from the data provided by industry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.1

Distillation column flow and composition information . . . . . . . . 61

3.2

Plant data Vs Simulation results . . . . . . . . . . . . . . . . . . . . 63

4.1

Results of the steady state simulation from [Udugama et al. (2015)] 87

4.2

Integral Absolute Error of XD and β for a feed disturbance (expressed as a percentage of absolute integral error of the proposed control) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

4.3

Integral Absolute Error of XD and β for a feed methanol disturbance (expressed as a percentage of absolute integral error of proposed control) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

4.4

Process and manipulated variable fluctuation for feed flow fluctuation105 xix

. . . . . . . . . . . . . . . . . . . 35

4.5

Average values of the variables displayed in Figure 4.10 . . . . . . . 106

4.6

Process and manipulated variable fluctuation for feed methanol fluctuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

4.7

Process and manipulated variable fluctuation for feed ethanol fluctuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

4.8

Comparison of energy consumption at different set points for the composition controller . . . . . . . . . . . . . . . . . . . . . . . . . 110

5.1

Current operating values . . . . . . . . . . . . . . . . . . . . . . . . 130

5.2

Annual change in net profit (proposed control (without backup) verses the current control strategy (in USD) . . . . . . . . . . . . . 136

5.3

Annual net benefit proposed control scheme (with back up) vs current control Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 139

5.4

NPV of the two control options . . . . . . . . . . . . . . . . . . . . 139

A.1 Values of the input variables for both points of operation. . . . . . . 150 A.2 Response variables for standard and increased recovery without disturbance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 B.1 Results of the steady state simulation from [Udugama et al. (2015)] 160 C.1 Maximum values of step test. . . . . . . . . . . . . . . . . . . . . . 181

xx

List of Symbols B D e F M˙ B M˙ D M˙ F M˙ F,M eth M˙ S M˙ S,M eth M˙ V M˙ W Q˙ Reb R S V W XB XD XD,M eth XF,Eth XF,H2O XF,M eth β

Bottoms Product Distillate Effect Feed Mass flow rate of the bottoms kg/h Mass flow rate of the distillate kg/h Mass flow rate of the feed kg/h Methanol Mass flow rate in the feed kg/h Mass flow rate of the side draw kg/h Methanol Mass flow rate in the feed kg/h Boil-up rate primary separation kg/h Boil-up rate seconcary separation kg/h Reboiler duty MW Reflux Ratio Side Draw Boil-up column Boil-up primary separation Methanol mass fraction at the bottom Ethanol mass fraction at the top Methanol mass fraction at the top Ethanol mass fraction in the feed Water mass fraction in the feed Methanol mass fraction in the feed Methanol recovery ratio -

xxi

Chapter 1 Introduction & Background 1.1

Prologue

Improving the performance of a high purity, multi-component industrial methanol distillation unit is a fine balancing act. On one hand, the operators need to ensure that ethanol impurities in product methanol are kept below a strict parts per million (ppm) specification, whilst on the other hand, the operators need to minimize the use of reboiler duty while maintaining an adequate product recovery ratio (β). With a lack of advanced control, the operators have no other choice than to use “excess reboiler” duty to offset effects of complex process dynamics and maintain these demanding specifications, limiting the levels of recovery and reducing column throughput and/or column stability. For example, the Methanex corporation, which is currently the world’s largest producer of methanol, is also in this situation [Kirkpatrick (2015)]. In previous decades, the financial trade-off between implementing modern advanced process control as opposed to classical control (“excess reboiler”) favoured the latter. With tighter operating margins and a dynamic energy market, these decisions warrant re-examination. In the rest of this chapter, relevant background information will be outlined, that includes an introduction to the methanol production process, factors that effect distillation column operations, process simulation and control techniques. A general literature review will then be presented, describing the current state of high purity, high recovery distillation column control. The literature review will also show the gaps in current research work that will make their implementation in an industrial plant difficult. This will be followed by a statement of the research aims and how each chapter addresses these objectives and the novelty of the work conducted. 1

2

Chapter 1. Introduction & Background

1.2

Methanol Production Process

The industrial production of methanol consists of three main steps: • Reforming (production of syngas) • Methanol Synthesis (conversion of syngas to methanol) • Purification (distillation) [Lee et al. (2007), Methxanex (2015)] The production of methanol begins with the reforming process, where a non renewable hydrocarbon source, such as natural gas (CH4 ) or coal or a renewable source such as biomass, is converted into synthesis gas (syngas) at high temperatures and moderate pressures [Lee et al. (2007)] . The generated syngas is then fed into a plug flow reactor, where it is converted to crude methanol, while the un-reacted syngas is recycled back. Finally, the crude methanol is refined in a distillation step to produce federal AA grade methanol. In industrial scale processes, methanol is mainly produced using natural gas (CH4 ) as feedstock [Methxanex (2015)]. Figure 1.1 gives a simplified overview of the industrial methanol production process.

Figure 1.1: Simplified schematic of methanol production


1.2.1

3

Reforming

Reforming is the first step in methanol production and is a common unit operation for many other chemical processes, such as ammonia production. In terms of process chemistry, there are a few pathways (recipes) that can be followed [Suib (2013), Gupta (2008)]: • Steam reforming, whereby syngas is produced by reacting methane and water (Equation 1.1) CH4 (g) + H2 O(g) CO(g) + 3H2 (g)

(1.1)

• Dry reforming (also known as CO2 reforming), whereby syngas is produced by carbon dioxide and methane ((Equation 1.2) CH4 (g) + CO2 (g) 2CO(g) + 2H2 (g)

(1.2)

• Oxy reforming process, whereby syngas is produced by reacting oxygen and methane (Equation 1.3) CH4 (g) + 0.5O2 (g) CO(g) + 2H2 (g)

(1.3)

Many large scale methanol producers (including Methanex) uses the steam methane reforming route, but would either purchase low quality (low BTU) natural gas feedstock that contains CO2 impurities or inject CO2 which is either captured or produced on-site, to the reformer in order to increase the production rate [Kirkpatrick (2015)] . With the addition of CO2 , the reactions within an industrial reformer become more complicated. Equations 1.4, 1.5, 1.6 describes possible equilibrium reactions that can occur.

CH4 (g) + H2 O(g) CO(g) + 3H2 (g)

(1.4)

CH4 (g) + CO2 (g) 2CO(g) + 2H2 (g)

(1.5)

H2 O(g) + CO(g) CO2 (g) + H2 (g)

(1.6)

4


To achieve optimal methanol production rates, the industrial methanol producers prefer Syngas exiting the reformer to have a CO +CO2 : H2 ratio described in equation 1.7 [Kirkpatrick (2015)].

2CO + 3CO2 ≤ H2

(1.7)

A simplified diagram of the reforming process is illustrated in Figure 1.2, where feed gas consisting of CH4 and CO2 is mixed with steam and is converted into syngas consisting of CO2 , CO and H2 . For steam methane reforming, the reformer needs to operate at a low pressure (10-20 Bar) and at temperatures of ∼ 850◦ C[Suib (2013), Gupta (2008)]. The reforming process consumes the largest fraction of energy in methanol production; substantial amounts of natural gas need to be combusted to achieve the high temperatures required for syngas production. The heat recovery systems in place allow for a large amount of high pressure steam to be raised by both cooling down the exhaust gas (flue gas) and the product syngas.

Exhaust gas

Feed Gas (CH4,CO2) Steam (H2O)

High Pressure Steam

Reformer 850 °C 10-20 Bar

Syngas (H2,CO,CO2)

Natural gas for heating Air for combustion

Figure 1.2: Reforming Proces

Heat Recovery

Syngas to Converter


1.2.2

5

Steam Loop

In many methanol production plants, steam is used for heat recovery and integration. The steam system in a methanol production plant can be divided into four main categories of: • Boiler feed water / condensate. • High-pressure steam. • Medium-pressure steam. • Low-pressure steam. Prior to exploring the steam production process and its uses, it is important to consider the boiler feed water system. The actual treatment process to convert “normal” water into boiler feed water is complicated and consists of multiple steps [Amjad (1994)]. Therefore, to reduce both the cost of producing “fresh” boiler feed water and environmental impact of plant operations, boiler feed water is mainly made of condensed steam that has been recycled. Boiler feed water is converted to steam at the reformer, where the boiler acts as either a cooler for the reformer wall or the syngas product stream. The relatively high temperature of the reforming process allows high pressure steam (HPS) to be raised. HPS is then superheated and primarily used to drive large compressors such as the compressor used to pressurize syngas between the reformer and the synthesis loop. The HPS temperature and pressure are tightly regulated. It is also important to note that auxiliary gas burners in the reformer can be used to provide limited HPS control, even during unstable process conditions. Medium pressure steam (MPS) is made up from steam extracted from large turbines (where HPS is the feed to the compressor). The medium pressure steam is used to drive smaller compressors. Similar to HPS, the MPS pressure and temperature are well regulated, even during unstable process conditions. The low pressure steam (LPS) is made up of steam extracted from both large and small turbines. Methanol refining units at the plant are one of the major users of LPS, where it’s used to provide heat energy in the form of reboiler duty. Unlike HPS and MPS, the LPS pressure and temperature are not regulated. LPS is often used as a “steam dump” where disturbances in the HPS and MPS are rejected to LPS, especially during process upsets.

6


LPS upsets LPS upsets can be caused by a multitude of factors. For example, an upset in the reformer can trigger lower quality steam to be raised at the boilers, at which point the controllers will reject this disturbance to LPS while attempting to keep HPS steady. Based on plant data from Methanex New Zealand between 2009 and 2013, steam loop upsets do happen a handful of times a year. During major steam loop upsets, the stability of LPS is sacrificed in order to keep the relatively more important HPS and MPS stable. Since LPS is used in the distillation process, this action creates potential control issues. Fluctuations in LPS cause minimal safety concerns to the plant and equipment, but can have significant economic implications. Chapter 5 specifically deals with the economic implication of these scenarios.

1.2.3

Methanol Synthesis

In the methanol synthesis loop, syngas produced in the reformer is converted into methanol. Unlike the reformer, the synthesis loop operates at high pressures (∼ 100 Bar) and at lower temperatures (∼ 250◦ C)[Brown (2011)]. In this process both methanol and water are produced as indicated by equations 1.8 and 1.9 [Brown (2011)]

2H2 (g) + CO(g) CH3 OH(g)

(1.8)

3H2 (g) + CO2 (g) CH3 OH(g) + H2 O(g)

(1.9)

In addition to these main reactions, ethanol is also produced by a side reaction [Brown (2011)]. This side reaction is of critical importance to industrial methanol manufacturers because refined product methanol, which is supplied to customers cannot contain more than 10 ppm wt of ethanol impurities. With recent advances in catalyst selectivity, crude methanol produced at the synthesis loop now contains ≤ 150 ppm of ethanol. In addition to ethanol, the synthesis loop also produces the following compounds: • Butano1 (ppm levels) • Organic acids and hydrocarbons (ppm levels)


7

• Di-methyl ether and tri-methyl amine (ppm levels) The methanol synthesis loop consists of a quench flow reactor, cooler and a separator, as illustrated in Figure 1.3. The recycle stream gas is mixed with “fresh” syngas from the reformer before entering the methanol synthesis reactor, where partial conversion of the incoming gas is achieved. The quench flow reactor often contains 4-5 beds of catalyst in a reactor where progressive methanol synthesis is achieved and heat is generated due to the exothermic nature of the reaction. The gas exiting each catalyst bed is diluted with “fresh” syngas to moderate the temperature of gas entering the next catalyst bed. Products from the methanol synthesis reactor are then cooled and introduced to a separator, where non-condensible un-reacted gas is separated from crude methanol. The crude methanol is then pumped into the distillation process, while the un-reacted gas is recycled back into the synthesis reactor.

Figure 1.3: Methanol synthesis process

1.2.4

Methanol Distillation

The crude methanol produced in the synthesis process is first pumped into a topping column, prior to being pumped into the main distillation/refining column, as illustrated in Figure 1.4. The topping column removes un-reacted gases and other light substances that were not removed by the separator in the methanol synthesis loop. The main purpose of the topping column is to ensure that dissolved gases and other light substances are removed from the crude methanol, as refined methanol can only contain trace levels of these gases. In general, the topping column carries out a simple, non-condensible gas separation and is “oversized”

8


[Kirkpatrick (2015)]. As such, topping columns will not be studied in detail in this thesis.

Figure 1.4: Methanol distillation process, Picture courtesy of Methanex Corporation

1.2.5

Alternative Configurations

The configuration of the methanol refining process illustrated in Figure1.4 is the standard general configuration used by many methanol producers and is the design of the methanol refining unit that was used as the case study in this thesis. However there are other configurations of methanol refining processes that are also used in industrial facilities worldwide. Figures 1.5 ,1.6 & 1.7 displays these alternative configurations.

9


Lights

Crude Methanol

Toping column

Methanol

Refining column I

Refining column II

Rec column

Fusel

Water

Figure 1.5: Alternative methanol refining configuration with 4 columns from [Thyssenkrupp (2017)]

The configuration in Figure 1.5 is developed by Thyssenkrupp Industrial Solutions [Thyssenkrupp (2017)] and is relatively more complex than the standard configuration. In this particular configuration there are two dedicated refining columns to produce methanol while a dedicated recovery column helps achieving a split between fusel oil and methanol. As such, the same workload that is undertaken by a single refining column in the standard configuration is distribute over three columns, which should allow for easier operation and higher levels of product recovery rates.

Lights Methanol

Toping column Crude Methanol

Refining column

Rec column

Fusel

Water

Figure 1.6: Alternative methanol refining unit with a 3 column configuration

10


The configuration in Figure 1.6 in comparison is much more similar to the standard configuration and is a simpler design. In this instance an extra refining column is introduced between the toping column and the recovery collumn to extract AA grade methanol. Another alternative configuration for a methanol refining unit is displayed in Figure 1.7 and is also very similar to the standard configuration. In this instance the fusel draw from the main refining column is introduced to an extra recovery column to further recover product methanol and achieve higher levels of overall recovery.

Lights Methanol

Crude Methanol

Toping column Rec column

Refining column

Fusel Water

Figure 1.7: Alternative methanol refining configuration with an extra recovery column

In comparison to all the industrially available alternatives the standard configuration is the most intensified form of recovering AA grade methanol at high recovery rates . As such, from a cost point of view the standard configuration will be have the least capital expenditure, although the configuration in Figure 1.7 is also a cost effective option [Kirkpatrick (2015)]. However, from a control point of view the standard configuration is the most difficult to control. This is due to the fact that in a single column the standard configuration requires to produce methanol at high recovery, meet a bottoms specification and extract fusel at a relatively concentrated level. It is also important to note that insights developed in this thesis based on the standard configuration can easily be transferred to the configurations in Figure 1.6 & 1.7 as both these configurations share a similarities and would face similar control issues. Other than the industrially available alternative configurations there area also alternative methanol refining configurations that have been discussed in literature.


11

These schemes have a varying degree of complexity[Suib (2013)].

1.3

Methanol Refining Column

To further explore the operations of industrial methanol refining, a typical industrial methanol refining column configuration, as illustrated in Figure 1.8, was investigated. The primary objective of the main methanol refining column is to make federal AA grade specification methanol, which has a strict Xf usel + B then profile resembles a “downward shift” • If Xf usel < C% then profile resembles a “blunt” It should be noted that X represents the ethanol concentration in weight whilst A, B and C will determine the point at which the Xf usel will be considered outside of its “normal operations” mode. Industrial methanol distillation columns studied in this work currently only have a single on line ethanol composition analysis at the the distillate. In order to reduce the implementation costs, a single composition analyser at the side-draw can be installed. This analyser will then be multiplexed to sequentially analyse samples at the side draw, tray 73 and 77. The relatively slow dynamics of the ethanol bulge as illustrated in Figure 3.6 means this method would be able to provide a sufficient sampling rate.

3.6.2

Side Draw Controller

Based on the above information we can then carry out the necessary control actions. In this particular work we have decided to alter the process variable signal the PID controller as follows. These equations describe how the ethanol bulge detection and the ethanol profile controller (EPC) illustrated in Figure 3.8 functions.

If X73 > Xf usel + A then Pseudo = Xf usel + X ∗

(3.2)

If X77 > Xf usel + B

(3.3)

If Xf usel < C

then Pseudo = Xf usel − X ∗∗ then Pseudo = X ∗∗∗

(3.4)

X ∗ , X ∗∗ and X ∗∗∗ can be calculated/ derived based on the process dynamics of the system and needs to be tuned to achieve optimum results. These logic operations are designed to achieve the following objectives: • If the composition of ethanol at the top sampling point is greater than the fusel draw plus A, excess the reboiler duty has pushed the profile upwards,

74

Chapter 3. Side Draw Ethanol Bulge Control thus the reboiler duty needs to be lowered. To achieve this the side draw ethanol composition signal will be increase by X ∗ prior to being sent to the PID controller. • If the composition of ethanol at the bottom sampling point is greater than the fusel draw plus B , the profile has shifted downwards due to a lack of sufficient reboiler duty, thus the reboiler duty needs to be increased quickly. This is achieved by decreasing the side draw ethanol composition signal by X ∗∗ prior to being sent to the PID controller. • The last logic is used to keep the ethanol profile sharp, where the bulge can get less defined due to lack of reboiler duty for a long period of time. Which, in turn, would create off specification bottoms methanol. In this instance the side draw ethanol concentration signal will be discarded and replaced with X ∗∗∗ . This last logic is a backup/safety logic as realistically the second logic should detect and remedy a lack of sufficient reboiler duty.

Once the structure of the control scheme was finalized it was necessary to tune the whole control structure. The PID controller will first be tuned for the process dynamics of the “normal” disturbance range using ATV tuning method. The process constants A,B,C,X ∗ ,X ∗∗ & X ∗∗∗ were first “coarsely” tuned based on the understanding of ethanol bulge behaviour. Then these constants were “fine” tuned based on trial and error to achieve the best mixture between aggressive control and robustness. The PID controllers tuning was then tightened based on step test responses. The results are displayed below • If X73 > Xf usel + 0.1% then Pseudo = Xf usel + 0.44% • If X77 > Xf usel + 0.1% then Pseudo = Xf usel − 0.44% • If Xf usel < 0.4% then Pseudo = 0.08%

3.6.3

Use in Other Applications

Based on the above work, we can propose the following general steps to set up a bulge control.

Chapter 3. Side Draw Ethanol Bulge Control

75

• Identify if the process response changes for different states of operations • Identify if the composition bulge is the reason behind these shifts. • Establish a method to detect movements/changes of the bulge. • Identify and establish a normal operating mode where a simple PID controller can be used to regulate the process • Develop a set of logic operations that can be used to identify the process is out of bounds • Use the process characteristics to define X values to bring the process back into the ’Normal operating mode’.

3.6.4

Distillation Composition Control

Using the SDC as the basis, we propose the following distillation column control structure illustrated in Figure 3.9. In this structure the side draw controller would be used set the reboiler duty to keep the ethanol side draw and ethanol bulge steady. While the ethanol in the distillate is controller by the distillate composition controller (XC). The XC act as a fine controller to control the ethanol composition in the top product while the SDC will control the ethanol profile along the column and manage the bottoms methanol composition. In terms of relative gain analysis (RGA) both controllers works with each other in achieving on specification column operations. In terms of tuning the distillate controller acts much faster than the side draw controller.

3.7

Controller Evaluation

The absolute limit of ethanol in high grade methanol is set at 10 ppm. Based on plant data and practical experience the composition controller for the product draw should be set to 7 ppm as this gives a 3 ppm buffer. Thus, the set point of the product ethanol control scheme would be set at 7 ppm initially. The set point of the fusel composition profile control is set at 0.66% based on the steady state mass balance: the fusel concentration needs to be around this value for the product ethanol value to be at 7 ppm specification.

76


Figure 3.9: Overall distillation composition control scheme with side draw controller

3.7.1

Set-point Change

To check the set point tracking ability of the overall composition control scheme, the product ethanol and fusel composition set points were changed while observing its effects on the ethanol composition at products and fusel. In Fig. 3.10, the set point at the product ethanol composition control was changed from 7 ppm to 10 ppm and down to 5 ppm. It is apparent that the product ethanol profile is capable of tracking this set point without exhibiting any bias. Figure 3.10 also clearly shows that the product composition controller has an influence on the fusel composition. This illustrates that the ethanol composition controller is quickly rejecting any process disturbances towards the middle of the column, where the SDC manages these effects without influencing the performance of the all important product ethanol specification. We also monitored the bottoms methanol composition during these set point changes which remained under 10

77

Chapter 3. Side Draw Ethanol Bulge Control ·10−5

·10−2 Product EtOH SP Prodcut EtOH PV

1.0

1.8

Fusel EtOH SP Fusel EtOH PV

0.8

1.4

0.6 1.0 0.4

EtOH Mass Frac. in Fusel

EtOH Mass Frac. in Product

1.2

0.6 0

0.5

1

1.5

2

2.5

3 Time (s)

3.5

4

4.5

5

5.5

6 ·104

Figure 3.10: Set point tracking characteristics of the proposed control scheme when the product ethanol specification is changed

ppm at all times. further analysing the graph we can observe the relatively large settling time of the side draw ethanol concentration when a product ethanol set point change is preformed.

3.7.2

Disturbance Rejection

In order to test the disturbance rejection skills of the proposed side draw control scheme, it was necessary to carry out feed flow and composition disturbance tests, as these are likely process disturbances that can occur in a real world situation. For comparison purposes, a standard DV control scheme was implemented on the distillation column. The DV control loop controlled the product ethanol specification at 7 ppm by manipulating the distillate flow rate (similar to side draw control) while the bottoms methanol composition was controlled by manipulating the reboiler duty was set up. The reboiler duty-bottoms methanol control loop has a set point of 3 ppm and was tuned with the ATV tuning method. Disturbance rejection tests were also carried out on a column with no control as it was thought to be beneficial for comparison purposes. Initial analysis into this matter showed stable operations can be achieved during steady state, but unsatisfactory disturbance rejection performance was observed, even during minor process fluctuations.

78


Feed Flow Disturbance In order to assess the feed flow disturbance rejection characteristics of the side draw controller and the DV controller, the feed flow rate was stepped up by 10 t/hr and was then stepped down back to the starting flow rate. In Figure 3.11, We have plotted the response of product ethanol composition, side draw ethanol composition and the bottoms methanol composition to these step changes. (a) Top EtOH concentrations 1.6

10 DV

9

Side-draw control Feed flowrate

8

1.5

7 0

1,000

2,000

3,000

4,000

1.4

(b)Fusal EtOH concentrations

·104

1.6 DV

1.5

Side-draw control

1

Feed flowrate

1.5

0.5 0

0

1,000

2,000

3,000

4,000

1.4

(c) Bottom MeOH concentrations

Feed flowrate (ton/hr) (×105 )

Mass Fractions (ppm)

6

1.6

40

DV Side-draw control Feed flowrate

20 0

0

1,000

2,000 3,000 Time (Second)

4,000

1.5 1.4

Figure 3.11: Response of the Side draw control and DV control to feed flow disturbances

Analysing Figure 3.11 (a) shows that both control schemes are able to tightly maintain the all important product ethanol specification for both step up and step down changes in feed flow. In contrast, Figure 3.11 (c) shows the bottoms methanol concentration of the DV controlled column spiking above 45 ppm when stepping up. This is significantly higher that the maximum allowable 10 ppm limit. The bottoms concentration also takes a significant time to settle back down to steady state during both the steps up and steps down. When the feed flow is stepped back down, the response of the bottoms methanol concentration for the DV controlled column has a much smaller spike (with-in the 10ppm limit). A similar period of time is required to settle back down to steady state, however. In contrast, the bottoms methanol response for the column with SDC control is stable around 3 ppm, with some minor initial fluctuation. Analysis of Figure 3.11 (b) yields a similar conclusion where the side draw ethanol concentration of


79

the column with DV controls responds by swinging significantly for a long period of time before settling down, while the column with SDC control responds by fluctuating minimally before quickly stabilising for both the step up and step down. The observations of Figure 3.11 can be explained as follows: In attaining tight product ethanol control, both controller will reject the effects of product flow disturbance towards the bottom of the column. In the SDC column, this disturbance is “arrested” by the ethanol profile controller at the middle of the column. As such, the side draw ethanol concentration of the side draw controller is fairly constant and stable. This, in turn, ensures the bottoms methanol concentration is kept under the 10 ppm limit. In contrast, the side draw ethanol concentration for the DV controller fluctuates significantly for a long period prior to settling down, which unsettles the product methanol profile in the column. As a result, despite manipulating the reboiler duty to maintain the bottoms methanol and having a controller set point of 3 ppm, the bottoms methanol concentration will exceed 10 ppm for a step up in feed flow rate. From a commercial perspective, operating a DV type control scheme would require the bottoms of the column to be sent to bio treatment at all times, as a feed flow rate change potentially can send the bottoms methanol concentration to > 40 ppm. In contrast, the same step up and step down in feed flow rate has minimal effect on the bottoms methanol specification of the side draw control scheme.

Feed Ethanol Concentration Disturbance In order to assess the disturbance rejection characteristics of the side draw controller and the DV controller to changes in feed ethanol concentration, it was decided to step up the feed ethanol concentration by 100 ppm to 250 ppm from a starting value of 150 ppm and then step down to the starting value of 150ppm. In Figure 3.12, we have plotted the response of product ethanol composition, side draw ethanol composition and the bottoms methanol composition to these step changes. Analysing Figure 3.12 (a) shows that both control schemes are able to tightly maintain the all important product ethanol specification for both step up and step down changes in feed flow, as observed in Section 3.7.2. Analysis of Figure 3.12(b) shows that the response of the bottoms methanol concentration for the

80

Chapter 3. Side Draw Ethanol Bulge Control (a) Top EtOH concentrations 10 DV

9

Side-draw control Feed EtOH mass

8

Mass Fractions (ppm)

0

1,000

2,000

3,000

4,000

(b)Fusal EtOH concentrations

·104

DV

1.5

Side-draw control

1

Feed EtOH mass

250 200

0.5 0

150

0

1,000

2,000

3,000

4,000

150

(c) Bottom MeOH concentrations 15

Feed EtOH mass fractions (ppm)

200

7 6

250

DV Side-draw control

10

Feed EtOH mass

200

5 0

250

0

1,000

2,000 3,000 Time (Second)

4,000

150

Figure 3.12: Response of the Side draw control and DV control to feed ethanol disturbances

DV controller is similar to the one observed in section 3.7.2, but, in this instance, the initial ethanol spike for a step up in feed ethanol concentration is ∼ 12 ppm. In contrast, the response of the bottoms methanol concentration to the side draw controller is different from section 3.7.2, where increasing the feed ethanol concentration results in gradual increase in bottoms methanol concentration (ultimately settling around 5.5 ppm, as observed in further testing). Analysis of Figure3.12(c) illustrates that both the DV column and the SDC column settles down at a higher side draw ethanol concentration value, when the feed ethanol concentration is increased by 100 ppm, while settling back down to the starting value, when the feed ethanol concentration is brought back down to 150 ppm. The side draw ethanol concentration response of the SDC controlled column transits smoothly to a new steady state when the feed ethanol concentration is stepped up and down. In contrast, the side draw ethanol concentration for the DV controlled column fluctuates noticeably during the initial transition before settling down at a similar value to the SDC column. The observations of Figure 3.12 can be explained as follows: In attaining tight product ethanol control, both controllers will reject the effects of product flow disturbance towards the bottom of the column. For the side draw controller, the excess ethanol arriving in the middle of the column results in the ethanol profile near the side draw shifting downwards. This, in turn, triggers the logic in the ethanol profile controller to intervene in order to keep the ethanol profile near the


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side draw in acceptable shape. In this instance, the reboiler duty will be increased whilst the ethanol profile and the side draw would settle at a new steady state. The DV controller on the other hand would not react to the change in the side draw ethanol profile. As a result the downward shift in the ethanol concentration will drag the methanol profile lower, which, in turn, will result an initial spike in the bottoms methanol concentration. It should also be noted that these type of step changes in ethanol and methanol/water profile are unlikely in a real plant. However, step disturbances provide a rugged and standard test in which the performance of two control structures can be compared.

3.8

Conclusions

An investigation into a high purity multi-component methanol distillation column was carried out with the aid of a validated dynamic process simulation. Initial analyses revealed that the ethanol side draw concentration has complex process characteristics: it exhibits an inverse, non-linear time varying response and changes the initial process gain sign based on the state of operation. Further investigation illustrated the “root cause” of this complex process characteristic to the movement of the ethanol bulge near the side draw. Based on this information, it was concluded that movement of the ethanol bulge must be managed to achieve stable column operations. To achieve this objective, we developed a novel, practical ethanol bulge detection and an ethanol profile controller (SDC) based on the principles of override control. The SDC interprets information from three composition sensors near the side draw to determine the location of the bulge and then applies the necessary remedial action to bring the bulge back into position. The disturbance rejection tests which were carried out on the side draw controller illustrated that it is able to control the column within tight high purity commercial specifications and achieve tight side draw ethanol concentration control. The same disturbance tests carried out on a column with a DV type controller was unable to achieve these tight high purity specifications and was not able to achieve tight control of the side draw ethanol concentration.

Chapter 4 High Recovery Control Structures 4.1

Prologue

In this chapter we focused on control structures that can guarantee on-specification products, while maintaining high rates of recovery. Control structures created in this Chapter are ideally suited for plants where distillation is limited by the crude methanol availability (Category 2 plant operations as described in section 1.3.2). To achieve this goal we first re-examined the process dynamics at high recovery (99.5%). We then used this information to develop a novel simple control structure. we also implemented model predictive control (MPC) on the column. In addition to standard step tests the system was put through sinusoidal disturbances with varying amplitudes to simulate process and disturbance cycles experienced by the column in the “real world”. A financial based analysis was also carried out to understand if the marginal improvements in energy efficiency justifies the capital, operating and training costs that will be incurred during the implementation of an MPC. This idea of financial based control analysis has been further developed in Chapter 5. In Chapter 2 the idea of lowering side draw location was proposed to improve product recovery, however, further dynamic analysis conducted illustrated that the lowering of the side draw was not feasible as such, this idea was not further perused. The idea of reducing or changing the mass flow rate to improve the recovery ratio which was also proposed in Chapter 2 was however, employed in this chapter.

83

84

4.2

Chapter 4. High Recovery Control Structures

Abstract

In this work we have developed a novel, robust practical control structure to regulate an industrial methanol distillation column. This proposed control scheme is based on a override control framework and can manage a non-key trace ethanol product impurity specification while maintaining high product recovery. For comparison purposes, a MPC with a discrete process model (based on step tests) was also developed and tested. The results from process disturbance testing shows that, both the MPC and the proposed controller were capable of maintaining both the trace level ethanol specification in the distillate (XD ) and high product recovery (β). Closer analysis revealed that the MPC controller has a tighter XD control, while the proposed controller was tighter in β control. The tight XD control allowed the MPC to operate at a higher XD set point (closer to the 10 ppm AA grade methanol standard), allowing for savings in energy usage. Despite the energy savings of the MPC, the proposed control scheme has lower installation and running costs. An economic analysis revealed a multitude of other external economic and plant design factors, that should be considered when making a decision between the two controllers. In general, we found relatively high energy costs favour MPC.

4.3

Introduction and Background

The determination of control system structure is an important step in distillation control. In general, there are two main branches of control structures available: a centralized multi-input multi-output (MIMO) controller, or a set of single-input single-output (SISO) controllers. Either one of these systems can be used to control a distributed or decentralized process. In the chemical industry, a decentralized type of control system is more common than the centralized control system, as it has more captivating advantages: it is easy to understand, uses uncomplicated hardware, and employs simple working algorithms [Morari and Evanghelos (1989), Marlin (2000), Svrcek et al. (2014)]. However, decentralized control schemes lack the ability to operate a system at optimal levels. Conversely, centralized types of controls, in particular Model Predictive Controllers (MPC), have the potential to operate systems at optimal levels. Despite this, there are limitations to using centralised controllers, some of these limitations are: higher


85

maintenance cost, difficulties in operation, complicated structure and lack of flexibility that can result in a fragile controller that is not profitable [Camacho & Alba (2013a), Alford (2002), Van den Boom & Backx (2005)]. In addition, the actual data gathering process required to create an MPC model can be difficult to carry out in an industrial environment and might require techniques other than simple plant step tests [Hay et al. (2005)]. Once the relevant data is gathered, the design and tuning process of MPC type controllers are also more complex and time consuming [Luyben (2010)]. Even with recent advances in computer technology, the field implementation of MPC still requires specialist high-end computer platforms [Taube (2015)]. In methanol distillation a multi-component feed of methanol, water and ppm levels of ethanol, is refined to achieve a tight high purity product and bottoms (ppm levels) and high methanol product recovery (β) (≥ 97.5 %). For decades, operators and control schemes only focused on maintaining product ethanol specification (XD ) below the industrial AA grade limit of 10 ppm [Cheng (1994)], while β was not actively pursued. As a safety buffer, the operators also used excess reboiler duty to create a higher than required reflux ratio which, in turn, produced product methanol with a XD of ∼ 4 ppm. This action provides the operators with a sufficient XD buffer and can negate the non-linear behaviours of the column. With plant management’s goal of improving profitability, these distillation columns now need to operate at improved product recoveries (∼ 99.5 %) using “normal” levels of reboiler duty while maintaining the AA grade product methanol specification. New control structures will need to be developed to meet this objective of increasing profitability. To operate the column at high purity and recovery while minimizing the use of reboiler duty requires a control structure that can deal with non-linear behaviour, where a small deviation from the operating point can have a significant influence on the operability and process dynamics . As such, a linear process model and linear SISO control structure might be insufficient to describe the system dynamics and control of the column over changing operating conditions [Fuentes & Luyben. (1983), Qin & Badgwell (2002)]. Thus, a classic distillation column control arrangement, such as DV control [Green & Perry (2008)], can have difficulty in controlling this type of process. In [Udugama et al. (2015)], these difficulties were confirmed; DV control was unable to operate an industrial methanol distillation process at required specifications. In contrast, MPC, which was specifically developed to control complex multi-variable processes [Qin & Badgwell (2002)] should

86


be able to control the column at new specifications . In recent years much research has been carried out on implementing MPC on complex distillation units. In [Yamashita et al. (2016)] the authors had looked into the tuning parameters of MPC for an industrial crude oil distillation unit. In [Iqbal & Aziz (2012)], the authors compared a MPC and decoupled PID control structure on a Methyl Tert-butyl Ether distillation process and found MPC was able to better control the process. In [Wojsznis et al. (2007)], the authors formulated a multi-objective optimization procedure to operate a heavy crude oil fractionator. Despite these applications and improvements, the drawbacks described previously means MPC might not be the best choice commercially. Thus, there is an incentive in the methanol industry to develop control structures that use decentralized control schemes that can operate methanol distillation columns at high recovery and high purity without the use of excess reboiler duty. The conceptual framework of override control might be one alternative to deal with the complex process dynamics of methanol distillation. Override control is generally used to either protect equipment from harmful conditions or to detect and correct faulty process variables from effecting controllability [Svrcek et al. (2014), Wade, H. (2004), Ramagnoli & Palazogl(2006)]. In both these variations, the override control “sits” above the regulatory layer and uses logic operations in determining if an intervention is necessary [Ramagnoli & Palazogl(2006)]. In this manuscript, we have developed a novel, robust, practical and easy to maintain control structure, that is able to meet the tight specifications of industrial methanol distillation without the use of excess reboiler duty. The proposed scheme has two layers that consist of a PID based regulatory layer and a supervisory layer inspired by override control. The regulatory layer, together with supervisory control maintain both XD and β at their tight specifications. In this control structure, the supervisory layer addresses the “root cause” of the non-linear process dynamics and allows the PID regulatory layer to control the process. For comparison purposes, a generic MPC with a discrete non linear process model was also developed. Once completed, both the proposed control strategy and the MPC were tested against a range of process disturbances. To conclude, the economic implications of choosing between MPC and the proposed control scheme were discussed.


4.4

87

Set-up

In methanol refining, a multi-component feed (methanol, water, ppm level of ethanol) is refined to achieve tight high purity product and bottoms at high methanol recovery ratio (≥ 97.5 %). In this paper, we have looked into developing control schemes that can operate the column at ≥ 99.5 % recovery and below the 10 ppm AA grade industrial methanol specification. The feed conditions for the high-purity column are given in Table 4.1. Table 4.1: Results of the steady state simulation from [Udugama et al. (2015)]

Feed

Distillate

Fusel

Bottom

Specifications

Value Units kg Mass Flow 142500 h ◦ Temperature 80 C Pressure 170.8 kPa Methanol Content 82.5 wt% Water Content 17.5 wt% Ethanol Content 150 ppm kg Mass Flow 115000 h Methanol Content 99.99 wt% Water Content 0.1 ppm Ethanol Content 8 ppm kg Mass Flow 3015 h Methanol Content 84 wt% Water Content 16 wt% Ethanol Content 0.61 wt% kg Mass Flow 25485 h Methanol Content 5 ppm Water Content 100 wt% Ethanol Content 0 ppm Reflux Ratio 1.687 Reboiler Duty 88.25 MW Methanol Recovery 97.5 %

To produce AA grade methanol, the distillate needs to have a methanol purity of 99.85 wt% and, more importantly, < 10 ppm ethanol. There are also limits on other trace compounds [Cheng (1994)]; but these compounds are distilled out of the crude methanol prior to its arrival in the main methanol refining column. Bottom discharge mainly consists of water and must not contain more than 10 ppm of methanol to satisfy waste water discharge restrictions. To accomplish such a high distillate and bottoms purity, most of the ethanol entering the column needs to be taken out via the fusel side draw. To satisfy both the operating stability

88


of the column and limitations on the disposal system, the mass flow of this fusel . stream must be kept within 2600 − 3700 kg h To maximize the profitability of the methanol production it is important to recover as much of the methanol entering the column as possible in the product methanol stream. In general the ratio of methanol in the feed to methanol in the product must be at a high level (≥ 97.5 %). In this paper we would call this the product recovery ratio (β) and define it as: β=

m ˙ M eOH,Dist · 100 % m ˙ M eOH,F eed

(4.1)

where the component mass flow rate of methanol in distillate is m ˙ M eOH,Dist and the component mass flow of methanol in the feed stream is m ˙ M eOH,F eed .

4.5

Modelling & Validation

Process modelling and simulation are well recognized tools for critical decision making, control design and optimization in process engineering [Svrcek et al. (2014), Udugama et al. (2014b)]. In this paper, the commercial simulation software packages VMGSim and HYSYS were used to build and validate dynamic models based on the industrial plant data shown in Table 4.1. Once validated, the simulation was pushed towards 99.5 % recovery and was then revalidated based on plant trials and other available data. The vapour liquid equilibrium (VLE) in the distillation column is described by an activity coefficient approach with a property package using the Wilson model. In this model, the activity coefficients (of each component in) the mixture were first determined to calculate the fugacity of the liquids, while a Virial equation of state was chosen to calculate the vapour fugacity. The column solver uses equilibrium and enthalpy models paired with rigorous thermodynamics in calculations. An overall tray efficiency of 80 % and a maximum flooding factor of 120 % is assumed. The basic PID controllers for pressure, flow and level control were also necessary to operate the column at a stable point.

89


4.6

Control configurations

In this section, we will introduce both the proposed control and MPC scheme designed for the high-purity methanol distillation column. To run the plant at a maximum profit, methanol recovery needs to be maintained at a high level, while satisfying the constraint of AA grade methanol in the distillate. In addition, the control schemes must also be able to operate the column in a stable manner. As such, both control schemes must meet the following requirements: • Maximizing/controlling methanol recovery • Handling the AA grade constraint on distillate ethanol content • Ability to reject disturbances in feed mass flow and composition

4.6.1

Process Dynamics

PPM Ethanol content in distillate

The requirement for high β and tight XD where non key ethanol needs to be managed at ppm levels, creates unusual non-linear process behaviours. To understand these non-linearities we have plotted in Figure 4.1 the response of XD to a change in β from a base case of 99.6 %.

14.0

β + 0.21%

12.0

β + 0.14%

10.0

β + 0.07%

8.0

β − 0.07% β − 0.14%

6.0

β − 0.21%

0

1,000

2,000

3,000

4,000

Time in min Figure 4.1: Process response of product ethanol specification for changes in product recovery ratio (β)

90


Figure 4.1 shows that the process has a non-linear response for all deviations in β. Analysing the +/- 0.07 % incremental recovery curves, we can see that a positive change in β has a higher influence on XD in comparison to a similar reduction in β. This confirms the non linearity of the system. The degree of non-linearity also tends to increase with bigger changes in β. Fundamentally, this behaviour is acceptable as larger positive deviations in β take the system closer to (and above) 100 % β at which point the imbalance in the material and energy balance will result in a surge of XD . This phenomena is further confirmed by the comparatively steep rise of the + 0.21 % incremental recovery curve. Bigger negative deviations in β, however, result in a smaller influence on XD as we are already at ppm levels of ethanol. Further analysis shows that the process itself has a relatively large time constant; even after 2000 minutes, most of the plots have not yet reached steady state From a practical perspective, we are more interested in the time taken for XD to exceed the 10 ppm constraint if kept unchecked as AA grade product methanol needs to contain < 10 ppm of ethanol . Even for a small increase in β of 0.14 %, XD will exceed the 10 ppm constraint over time. As the deviation is increased to 0.21 %, the time taken for XD to reach 10 ppm reduces dramatically.

4.6.2

Proposed Control Scheme

Control of a high purity industrial methanol distillation column is complicated due to the high purity product specification xD , high product recovery requirements and need to minimize the reboiler duty usage. In addition, the control scheme developed needs to be industrially compatible. Standard control structures available in literature are incapable of meeting all these requirements. As such, in this work we developed a novel control structure that can meet all these requirements. The proposed control scheme in this paper was developed with the aim of achieving high recovery and high purity while minimizing reboiler duty usage and in turn reducing the costs of operation. The proposed control scheme comprises of two layers: a regulatory primary layer and a supervisory secondary layer. The secondary layer consists of a recovery constraint controller (RC) and a dynamic reboiler controller (DR). We have used the control structure developed in [Udugama et al. (2015)] as a start point. We will call this proposed control scheme by the acronym RCDR in the following sections.

91


The RC controller acts as a central supervisory controller that takes measurements of the current recovery ratio and the ethanol content in the distillate stream. The set point of the composition controller YSP,QC will be set depending on the state of the system. To avoid sudden set point changes of the composition controller, a rate limiter is added to the output signal of the RC controller. The maximum rate of change is set to % 0.1 min . The calculation of the set point is based on two different cases according to the following equations. Recovery Constraint Controller - RC

β − βSP [%] β − βSP − ML · 10−6 · [%]

High recovery case: YSP,QC = YSP − MH · 10−6 ·

if βSP ≤ β

(4.2)

Low recovery case: YSP,QC = YSP

if βSP > β

(4.3)

Equation 4.2 represents the high recovery case, where the current recovery β is higher than the specified recovery set point (βSP ). In this case, the RC controller will take advantage of this beneficial situation and produce higher grade methanol with a ethanol content of < 7.8 ppm. Accordingly, the set point of the composition controller will be lowered. This action also ensures that the system is not running at recoveries of β > 100 % over long periods of time, which would be infeasible. MH , the gradient in Equation 4.2, dictates the rate at which the set point will be increased for a given increase in β. A large MH represents an aggressive controller. In this case we have set MH = 2. The low recovery case is represented by equation 4.3. If the system is running at a recovery of β < βSP , the RC controller will try to increase β by increasing the set point for the composition controller. The set point can only be increased up to the maximum ethanol content of 10 ppm. Unlike the high recovery case, the low recovery case represents a situation that is economically unfavourable. This requires the controller to intervene aggressively. To enable this, ML , the gradient in Equation 4.3, has been set to 6. Figure 4.2 shows a control schematic of the overall control scheme. The low recovery and high recovery equations ensure that the β is managed to be around 99.6 %. Even during sharp disturbances (as discussed in section 4.8), these equations will bring β back to set point before the non-linearities can effect the column. As such, these equations deal with the reasons for non-linearities

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rather than their effects.

Condenser PC

R Set Point & R (PV) input

RCC PT

XT FC XT

FT

XT

FT

Distillate FC XT

FT

Feed Refining Column

DRC

Side Draw

LT

Reboiler

LC

Bottoms

Figure 4.2: Schematic of the proposed control scheme (including regulatory controllers)

The flow rate of the fusel stream is also set by the RC controller element. The necessary fusel flow rate is determined by solving a material balance equation.

m ˙ F usel =

m ˙ EtOH,F eed XEtOH,F usel

(4.4)

In this equation, fusel mass flow rate (m ˙ F usel ) is determined by the mass flow of ethanol in the feed (m ˙ EtOH,F eed ) and the concentration of ethanol in the fusel draw (XEtOH,F usel ). Depending on the current feed composition, the amount of ethanol in the distillate and the fusel composition, an optimal fusel flow rate can be calculated according to equation 4.4. This flow rate is implemented as the set point of the fusel flow controller. Minimum and maximum set point values of


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2000 Kg/h and 3700 Kg/h respectively are enforced to reflect practical limitations. However, this mass balance only looks at the instantaneous ethanol feed flow and the fusel ethanol concentration. To remedy this shortcoming the required fusel mass flow calculation is averaged (as illustrated in Figure 4.3). This provides more stable operations as the ethanol accumulation and depletion at fusel side draw occurs over time and the averaging the calculated mass flow takes this into account. To better illustrate the function of the controller, we have constructed a control diagram of the RC controller in Figure 4.3. The RC controller consists of two parallel controller modules. The top module deals with setting the XD controller set point, where it uses the distillate methanol flow rate and feed methanol flow rate are used as process inputs (PVs). It also requires the plant operator to set the recovery ratio (β) and product ethanol (XD ) set point limits. In addition, a maximum ethanol limit also needs to be specified. Generally, this can be the AA grade methanol limit of 10 ppm. The dotted circle in this diagram shows the logic switch and equations used to determine the relationship between the recovery ratio and product ethanol specification at a given process condition. The fusel flow rate set point module is much simpler: feed ethanol mass flow and fusel ethanol concentration, together with maximum and minimum fusel flow limits, are used to determine the fusel flow set point. In addition to these functions, the RC controller also sends the current recovery ratio and recovery ratio set point information to the DR controller, as discussed below.

94


Figure 4.3: Control Diagram of the RC controller illustrating the inputs, outputs and the information flow within the controller. Where (1),(2),(3) and (4) refer to Equations 4.1 , 4.2 , 4.3 and 4.4 that are used in the RC controller calculations.

Dynamic Reboiler Controller - DR The DR controller comprises of a feed forward controller and a feedback trim controller. The feed forward controller increases or decreases reboiler duty based on the feed flow rate. Equation 4.5 has been empirically derived based on both plant performance and model output to achieve a 99.6% recovery at 7.5ppm product ethanol. m ˙ F eed Q˙ Reb = 0.6707 · h kg i − 4200 [kW]

(4.5)

h

In cases where feed disturbances can be measured, the feed forward controller can improve column stability. In addition, a simple feed back trim controller is used to keep track of and maintain β at set point by carrying out minor adjustments in reboiler duty. The feedback trim controller is a basic PI type controller. Employing a dynamic feedback controller which takes into account the process dynamics of the distillation unit would provide further improvements in the disturbance rejection characteristics of the overall control structure. However, the implementation of a dynamic feedback requires accurate quantification of process dynamics (process model), which can change as β changes. In comparison, the static feed forward is simpler, and would provide a good initial compensation which


95

can then be supplemented with feedback trim to provides sufficient performance, this is evident in the subsequent disturbance tests. Figure 4.4 illustrates the control architecture of the DR controller, where a feed forward element together with a feed back trim controller is used to set reboiler duty. The controller also takes current recovery and recovery set point information from the RC controller. The feed forward element in this instance acts as a “coarse” controller, while the feed back trim acts as a “fine” controller. This means a simple feed forward controller is sufficient as the trim controller can deal with correcting offsets and reacting to unmeasured disturbances. The trim controller also allows the DR controller to operate at different product ethanol specifications and at different recovery rates. Since the trim controller receives recovery information from RC controller, the users only have to set the desired product ethanol and product recovery ratio at the RC controller.

Figure 4.4: Control diagram of DR contoller illustrating the inputs out puts and the information flow with in the controller. Where (5) refers to Equation 4.5 used to in the DR controller calculations

Overall Performance The RC controller and DR controller work in combination to take advantage of the process characteristics described previously. In the case of a change in product recovery, the RC controller will actively influence the product ethanol set point to bring the product recovery back to acceptable values. In a situation of decreased product recovery, the RC controller will relax the product ethanol specification up to 10 ppm. This action allows the controller to restore the product recovery rate for a short time period, without breaking the

96


product ethanol specification. As such, the RC controller will “sacrifice” product ethanol stability to maintain recovery. However if, XD reaches 10 ppm, the RC controller will prioritise keeping XD within constraints. In a practical sense the RC controller “takes advantage” of the gap between the controller set point and industrial AA specification to manage disturbance situations and minimize the financial impact of disturbances on plant net revenue. Similarly, the DR controller would also detect a change in product recovery (or, in the case of feed disturbances, sense changes in product recovery) and increase reboiler duty appropriately to bring both recovery and product specification back to the set point. In general, the RC controller is able to maintain recovery until the DR controller can bring the system back to a stable steady state.

4.6.3

Model Predictive Control (MPC)

This section will give a more detailed overview of the implementation of the model predictive controller(MPC). The choice of tuning parameters for the MPC, as well as the tests conducted to obtain the dynamic process model will be discussed. The methanol distillation column can be controlled by a 2x2 multi-input multi-output (MIMO) controller, with each input affecting both outputs. The two inputs are the methanol recovery β and the ethanol content in the distillate stream. The recovery is calculated according to equation 4.1. The two outputs of the MPC are the set point for the XD controller and the reboiler duty.

Process model To build the process model, we carried out open loop step tests on our dynamic simulation, where we changed reboiler duty and distillate flow rate and observed its effect on XD and β. As expected, the process dynamics were relatively complex and could not be accurately estimated by a simple first order plus dead time model. Since industrial MPC modules allow the user to input the actual set response (step response data points that describes a discrete transfer function), we normalised our responses and created transfer functions that were then inserted into the MPC module as the process model.The MPC module used in this particular instance is a commercial system.

Tuning parameters and process variable One distinctive feature of MPC is the large amount of tuning parameters and variables that needs to be set [Svrcek


97

et al. (2014)]. The first variable that needs to be set is the control or sampling interval k. This denotes the time interval in which the MPC will take actions. The large time constant of the process means a long time interval between MPC execution would be sufficient to regulate the process. The sampling interval value k was first set to 100 minutes, as this is was 1/10 th of the time constant of the smallest process time constant. However, this resulted in unfavourable results during the short term disturbances. Thus a k value of 10 minutes has been set based on short term cyclical disturbance tests carried out. From a practical point of view, k of 10 minutes is still significantly long enough for the MPC application to execute and subsequent real time measurements needed (especially composition measurements) can be updated within this time frame. The prediction horizon P represents the number of sampling intervals into the future where predictions are made. The prediction horizon was set to P = 50 to account for the large time constant of the process. The control horizon M is the number of control moves into the future the MPC considers when predictions are made. The default value of M = 2 was chosen, as this gave the best responses in initial testing. The last parameters are the process variable and manipulated variable weighting matrices Γy and Γu respectively. We found that at low Γu values, the controller took large controller actions that affected its stability, but was better at rejecting sharp disturbances. Conversely, a high Γu resulted in stable steady state performance, but with bad disturbance rejection characteristics. Since we desire both stability and disturbance rejection properties we have set Γy and Γu to 1 which gives equal priority to both.

Implementation A schematic of the MPC scheme is shown in Figure 4.5. As previously described, the two inputs are the ethanol content in the distillate XD and the methanol recovery β. The recovery is governed by the distillate stream which is adjusted by the composition controller. The fast actions of this controller can lead to a noisy signals for the MPC. In order to reduce the signal noise, a rate limiter is used. The maximum rate of change on the recovery input signal is % . A material balance is solved at every sampling interval k following set to 0.1 min equation 4.4. The fusel flow in this instance was kept at the default value of 3113 kg h

98


Condenser MPC PC

LT

LC

PT

FC XT FT

XT

FT

Distillate FC

Feed

FT

Refining Column

Side Draw

LT

Reboiler

LC

Bottoms

Figure 4.5: schematic of the high-purity methanol distillation column with model predictive control (MPC)

It should also be noted that the implementation of a non-linear MPC can potentially improve the performance of the MPC installed. However in this particular scenario the gains were marginal. From a practical point of view the additional complexities bought on by the non-linear MPC solvers cannot be justified by the marginal performance increase. Similar comments can be made to regarding the addition of a feed forward disturbance model to MPC.

99


4.7

Feed step tests

To understand the behaviour of the control schemes to process disturbances, it was necessary to carry out disturbance step tests. Based on analysis and operators experience, it was determined that the feed flow rate and composition are the most likely process variables to introduce process disturbances into the column.

4.7.1

Feed flow rate step test

150.00 95.0 140.00 Reboiler duty MPC Reboiler duty RCDR

90.0

Feed flow rate 0

500

1,000 Time in min

1,500

Feed flow rate in T/h

Reboiler duty in MW

To start off with, the feed flow rate was changed from M˙ F eed = 142.5 ht to 147.5 ht . This represents an extreme scenario where it is demanded that the column take extra feed flow in a short period of time. The response of MPC and the proposed control scheme to this step test are illustrated in Figures 4.6 and 4.7 . Where the reboiler duty is the manipulated variable, while product ethanol composition and product recovery are process variables.

130.00 2,000

Figure 4.6: Reboiler duty response of MPC and proposed control scheme (RCDR) to changes in feed flow rate

100


Recovery

8.0 1.00

Recovery MPC Recovery RCDR Ethanol MPC Ethanol RCDR

0.99

0.98 0

500

1,000 Time in min

1,500

6.0

PPM Ethanol in distillate

1.01

4.0 2,000

Figure 4.7: Responses in β and XD of MPC and proposed control scheme (RCDR) to changes in feed flow rate

As expected, both the control schemes counter the increase in feed flow rate by increasing reboiler duty. The proposed control scheme reacts with an “impulse” like response due to the feed forward controller. The feedback trim controller then reduces the reboiler duty to achieve 99.6 % recovery. In comparison, the MPC controller has a far more gradual response and reaches a similar steady state as the proposed control scheme. This behaviour can be attributed to the ratio of Γy and Γu which is set at 1 (balanced approach). Both control schemes are able to tightly maintain XD and β, while the MPC returns to set point quicker than the proposed control scheme. It is also important to note the increase in XD specification for the proposed control scheme is rapid. This behaviour can be attributed to the proposed control scheme trying to “sacrifice” XD specification in order to maintain recovery at the desired set point of 99.6 %, This fact is further confirmed by analysing the results from Table 4.2. In this table the Integral absolute error (IAE) for both the proposed control scheme and the MPC is expressed as a percentage of the IAE of the proposed control scheme for that particular variable monitored.

101

Chapter 4. High Recovery Control Structures Table 4.2: Integral Absolute Error of XD and β for a feed disturbance (expressed as a percentage of absolute integral error of the proposed control)

Variable

Control Scheme

Integral Absolute Error

XD

Proposed Control MPC

100 % 38.5 %

β


100 % 450 %

Examining Table 4.2, we can conclude that, in terms of the recovery, the proposed control scheme does a better job at “arresting” the initial drop in recovery. As explained, the nature of the proposed control scheme is to “sacrifice” XD in the short term, which, in turn, enables β to quickly return to specified recovery. However, this quick recovery comes at the cost of XD stability where the total integral error for MPC controller is much lower than the proposed control scheme.

4.7.2

Feed Methanol Content Step Tests

In this test, we have increased the methanol concentration of the feed stream by 2 wt%. To compensate, the water concentration in the feed has been decreased by 2 wt%. This represents an extreme situation where, a sudden change in feed gas or catalyst has rapidly changed the methanol concentration.

95.0

0.85

90.0 Reboiler duty MPC Reboiler duty RCDR Feed methanol content

85.0 0

1,000

2,000 Time in min

3,000

0.80

Methanol fraction in feed

Reboiler duty in MW

0.90

0.75 4,000

Figure 4.8: Reboiler duty response of MPC and proposed control scheme (RCDR) to changes in feed methanol content

102


Ethanol MPC Ethanol RCDR

Recovery

1.01

8.0 1.00 6.0 Recovery MPC Recovery RCDR

0.99

0

1,000

2,000 Time in min

3,000


10.0

4.0 4,000

Figure 4.9: Responses in β and XD of MPC and proposed control scheme (RCDR) to changes in feed methanol content

Analysing Figures 4.8 and 4.9 shows both the MPC and proposed control scheme has increased the reboiler duty to deal with the excess methanol concentration. Thermodynamically, this makes sense as an increase in methanol concentration means a higher mass of methanol needs to be distilled to keep the recovery ratio at specification . Both control schemes have similar rise times while the proposed control scheme seems to be increasing the reboiler duty slightly more than MPC in the initial stages, but returns to a similar steady state. In feed flow disturbance testing, we looked at how the two control schemes react to a measured disturbance, as the proposed control scheme had some degree of “advanced” information through the feed forward controller. By contrast, in the methanol content step test, both control schemes get the same information through the process variable β. As such both control schemes are on an “equal footing”. In terms of ethanol ppm, the proposed controller seems to have a bigger “swing” in ethanol concentration in comparison to MPC. Table 4.3 confirms this observation, as MPC has a lower integral absolute error in controlling XD . However, this is an expected outcome, as the proposed control scheme sacrifices the short term ethanol ppm stability (up to 10 ppm) to stabilize the recovery. This is also evident as MPC has a bigger “swing” in recovery in comparison to the proposed control scheme and has a higher integral error in controlling β. This test clearly shows that the proposed control scheme can better stabilize the recovery (which is of financial importance), while MPC is better at stabilizing the product ethanol specification.


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Table 4.3: Integral Absolute Error of XD and β for a feed methanol disturbance (expressed as a percentage of absolute integral error of proposed control)

Variable XD β

4.8

Control Scheme Proposed Control MPC Proposed Control MPC

Integral Absolute Error 100 % 38.5 % 100 % 180 %

Disturbance Tests

In order to compare the different control schemes in response to real world conditions, their performance under cyclic process disturbances was tested. The tests were designed to reflect realistic disturbance scenarios in plant operation. The main source of disturbances in this particular process is the feed stream. Three different series of disturbance tests were performed on each control system. They were feed mass flow variations, feed methanol/water ratio variations, and finally, feed ethanol content variations. In [Udugama et al. (2015)], we carried out similar feed flow disturbance tests on a DV control configuration. These tests illustrated that a DV configuration cannot maintain the recovery or meet the bottoms methanol composition, which must be met in operations. For comparison, both proposed control scheme and MPC controllers are able to maintain this composition specifications.

4.8.1

Disturbances in Feed Flow Rate

To compare the ability of the two control schemes to handle oscillating disturwas bances in feed flow rate, a sinusoidal variation of m ˙ F eed = 137500 − 147500 kg h introduced. To model both long time period process variations and short time period plant upsets, two different time periods of tshort = 50 min and tlong = 2000 min were examined. Figure 4.10 shows the controlled variables XD , β and the reboiler duty Q˙ Reb for the MPC and the proposed control scheme.

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Reboiler duty MPC Reboiler duty RCDR Feed flow rate

0

1,000

2,000

3,000

140

90

Reboiler duty MPC Reboiler duty RCDR

85

130 4,000

0

20

Time in min

1.10

Recovery MPC

6

Recovery RCDR Ethanol MPC

10

8 1.00

0.90 3,000

6

Recovery MPC Recovery RCDR

0.95

Ethanol MPC Ethanol RCDR

Ethanol RCDR

2,000

100

1.05 Recovery

1.00


8

1,000

80

1.10

10

0

60

(b) Q˙ Reb and m ˙ F eed for tshort

1.05 Recovery

40

Time in min

(a) Q˙ Reb and m ˙ F eed for tlong

0.95

130

Feed flow rate


85

95

Feed flow rate in T/h

140

90

Reboiler duty in MW

95

150 Feed flow rate in T/h

Reboiler duty in MW

150

4 4,000

0.90

4 0

20

40

60

80

Time in min

Time in min

(c) β and XD for tlong

(d) β and XD for tshort

100

Figure 4.10: The effect of short time period and long time period feed flow rate fluctuations on reboiler duty, XD and β

For long time period fluctuations, it can be seen that there are only minor differences in both the energy input and the disturbance reactions of the MPC and the proposed control scheme, as illustrated in Figure 4.10a & 4.10c. However, for short time period fluctuations, as shown in Figure 4.10b, the reboiler duty for the MPC is out of phase by 15 minutes and the actions are very small compared to the proposed control scheme. These observations are confirmed in the amplitude of deviation results in Table 4.4, where all variables, except the short time period reboiler duty reaction, are the same. For short time period disturbances the proposed control scheme has a 7× greater amplitude in its reboiler duty reaction compared to the MPC controller. Closer observation of the tabulated results also shows that the proposed control scheme is slightly better at β fluctuations, while MPC is slightly better at controlling the ethanol specification.

105

Chapter 4. High Recovery Control Structures Table 4.4: Process and manipulated variable fluctuation for feed flow fluctuation

Process Variable XD amplitude (ppm) β amplitude (%) Reboiler amplitude (MW)

Control Scheme Proposed Control MPC Proposed Control MPC Proposed Control MPC

Long time period Short time period 0.19 0.88 0.30 0.11 0.05 0.34 0.36 0.37 4.19 3.28 3.12 0.30

This behaviour can be explained by analysing the way the two schemes work. The proposed control scheme uses a feed forward element that adjusts the reboiler duty directly with the feed flow rate. For this reason, the reboiler duty for both short and long time period disturbances will always reflect the change in feed flow rate for the proposed control scheme. MPC, on the other hand, measures changes in recovery and ethanol content in distillate to calculate the output. This means that disturbances in the feed have to affect β and XD before the MPC will take action. Since fluctuations in feed flow rate take some time to affect β & XD , the response in reboiler duty is out of phase by 1.5 minutes, compared to the proposed control scheme’s 15 minutes. For short time period disturbances this effect becomes visible. Also, the objective function of the MPC penalizes large control moves (changes in manipulated variables). Since the time period of the sine wave is only ten times bigger than the sampling interval, the MPC does not have sufficient time to carry out big changes in reboiler duty. The response of the controlled variables are displayed in Figures 4.10c & 4.10d and tabulated in Table 4.4. For both long and short time period disturbances, the recovery is almost constant and very close to the set point of β = 99.6 % for both MPC and the proposed control scheme. The deviation of XD in the distillate stream from set point is roughly the same for MPC and the proposed control scheme under long time period disturbances. Since MPC takes smaller control actions in reboiler duty for short time period disturbances, one would assume that MPC would preform poorly in controlling XD in comparison to the proposed control scheme. However, analysing Table 4.4 we can see that proposed control scheme preforms 8 × worse than MPC for XD control and only slight better at β control. We can conclude from this that the proposed control scheme slightly overreacts for short time period cyclical process disturbances. To remedy this issue, we can use a rate-limiter in the design of

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the DR controller, which would slow down the rate at which the reboiler duty can change. However, this type of a rate limiter will make the proposed control scheme more vulnerable during sharp feed flow disturbances as illustrated in section 4.7.1. The average values of the variables in Figure 4.10 are summarized in Table 4.5. Table 4.5: Average values of the variables displayed in Figure 4.10

Variable Feed flow rate in

kg h

Reboiler duty in kW XD in ppm Recovery β in %

Controller type MPC Proposed Control MPC Proposed Control MPC Proposed Control MPC Proposed Control

Average for tshort 142,538 142,538 91,300 91,126 8.0 8.1 99.62 99.60

Average for tlong 142,515 142,515 91,392 91,549 7.9 7.8 99.61 99.61

From the average values in Table 4.5, it can be seen that the reboiler duty used by both control schemes is the same. However, since MPC causes a smaller fluctuation amplitude in the all important XD variable, we can look to increase the XD set point to closer to the 10 pmm limit. Based on further analysis we have found 9 ppm would be an acceptable and reasonable new set point. The results and potential energy savings will be discussed in Section 4.9. In [Udugama et al. (2015)], we carried out similar feed flow disturbance tests on a DV control configuration. These tests illustrated that a DV configuration cannot maintain the recovery or meet the bottoms methanol composition, which must be met in operations. For comparison, both proposed control scheme and MPC controllers are able to maintain this composition

4.8.2

Disturbances in Feed Methanol Content

In this set of disturbance tests, the methanol content in the feed stream was varied from XM eOH,F eed = 80 % to XM eOH,F eed = 85 %. This disturbance was implemented as a varied methanol feed mass flow m ˙ M eOH,F eed . The mass flow of ethanol was unchanged, while the water mass flow was adjusted to ensure a . The time periods tshort and constant overall feed mass flow of m ˙ F eed = 142500 kg h tlong were used for the sinusoidal disturbance. Figure 4.11 and Table 4.6 show the

107


reaction of the proposed control scheme and MPC to short and long time period feed methanol variations.

0.80 90 Reboiler duty MPC

0.75

Reboiler duty RCDR Feed methanol content

85

0

1,000

2,000

3,000

95

0.85

0.80 90 Reboiler duty MPC

0.75

Reboiler duty RCDR Feed methanol content

85

0.70 4,000

0.70 0

20

Time in min

40

60

80

100

Time in min


(b) Q˙ Reb and m ˙ F eed for tshort 10

10

1.00 Recovery MPC

6

Recovery RCDR Ethanol MPC

8 Recovery

8

1.00

Ethanol MPC

Ethanol RCDR

0.95 0

1,000

2,000

3,000

6

Recovery MPC Recovery RCDR


1.05 PPM Ethanol in distillate

1.05

Recovery

Methanol fraction in feed

0.85

Reboiler duty in MW

95

0.90 Methanol fraction in feed

Reboiler duty in MW

0.90

Ethanol RCDR

4 4,000

0.95

4 0

20

40

60

80

Time in min

Time in min



100

Figure 4.11: The effect of short time period and long time period methanol concentration fluctuations on reboiler duty, XD and β

Table 4.6: Process and manipulated variable fluctuation for feed methanol fluctuation

Process variable XD amplitude (ppm) β amplitude (%) Reboiler amplitude (MW)

Control Scheme Proposed Control MPC Proposed Control MPC Proposed Control MPC

Long time period Short time period 0.12 0.39 0.17 0.10 0.09 0.10 0.24 0.39 2.33 0.18 1.99 0.32

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During the short term variations, both proposed control scheme and MPC show minor fluctuations in reboiler duty. This behaviour can be attributed to the rate limiter on proposed control scheme and the Γu of the MPC which prevents rapid changes in control variables. In general, both controllers are keeping XD specification and maintaining a normal recovery. During the long time period disturbances we can see both the controllers changing reboiler duty accordingly. In terms of the XD specification and recovery, we can see more variation during the short time period disturbances. Again, both controllers are maintaining specifications. A look at Table 4.6 also confirms that the key process and controlled variables are not fluctuating too much and have similar amplitudes of fluctuation. As expected, the proposed control scheme is doing a better job at controlling β while the MPC controller is doing a better job at controlling XD .

4.8.3

Disturbances in Feed Ethanol Content

In the last test series, the ethanol content in the feed stream was changed using a sinusoidal wave from XEtOH,F eed = 100 ppm to XEtOH,F eed = 200 ppm. The mass flows of the other components were unchanged, since variations in ethanol mass flow have no major effect on the overall feed mass flow. Figure 4.12 and Table 4.7 show the reaction of the proposed control scheme and the MPC to short and long time period feed ethanol variations.

100 Reboiler duty MPC

Reboiler duty in MW

90

PPM ethanol in feed

200

95

90

100 Reboiler duty MPC

Reboiler duty RCDR

Reboiler duty RCDR

85

85 Feed ethanol content

0

1,000

2,000

3,000

Feed ethanol content

0 4,000

0 0

20

Time in min

Recovery MPC

6

Recovery RCDR Ethanol MPC Ethanol RCDR

1,000

2,000

100

3,000

Time in min


4 4,000

1.02 8

Recovery

1.00


Recovery

8

0

80

(b) Q˙ Reb and m ˙ F eed for tshort

1.02

0.96

60

Time in min


0.98

40

1.00

0.98

6

Recovery MPC Recovery RCDR Ethanol MPC

0.96


Reboiler duty in MW

200

95

PPM Ethanol in feed

109


Ethanol RCDR

4 0

20

40

60

80

100

Time in min


Figure 4.12: The effect of short time period and long time period period feed ethanol concentration fluctuations on reboiler duty, XD and β

During the short time period variations, both proposed control scheme and MPC show no control actions. This behaviour can be attributed to the requirement for ethanol to accumulate/deplete for a long period of time to affect the column performance. For the long time period disturbances, both control schemes react in a similar manner, where reboiler duty is increased for increases in feed ethanol content and decreased for decreases in feed ethanol content. We can also see that both control schemes have a 200 minute lag.

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Chapter 4. High Recovery Control Structures Table 4.7: Process and manipulated variable fluctuation for feed ethanol fluctuation

Process variable

Control Scheme

XD amplitude (ppm)


0.19 0.11

0.22 0.04

β amplitude (%)


0.09 0.17

0.01 0.11

Reboiler amplitude (MW)


1.05 1

0.16 0.07

4.9

Long time period

Short time period

Energy Savings and Economic Benefits

In section 4.8, tests illustrated that MPC is able to control the XD tighter than the proposed control scheme during process fluctuations, while maintaining similar recovery. As such, the XD of the MPC controller can be pushed closer towards the 10 ppm industrial AA grade methanol specification, which enables the column to reduce reboiler duty usage. In further analyses we concluded that the XD set point for MPC can be increased up to 9 ppm. Table 4.8 shows the net energy benefit of using MPC over the proposed control scheme for this particular case. Table 4.8: Comparison of energy consumption at different set points for the composition controller

Disturbance Flow rate disturbance

XD Set point

Q˙ Reb for tshort

Q˙ Reb for tlong

7.84 ppm 9.00 ppm

91, 300 kW 90, 812 kW 488 kW

91, 392 kW 90, 867 kW 525 kW

7.84 ppm 9.00 ppm

91, 364 kW 90, 817 kW 547 kW

91, 454 kW 90, 841 kW 613 kW

7.84 ppm 9.00 ppm

91, 532 kW 90, 871 kW 661 kW

91, 398 kW 90, 858 kW 540 kW

Energy savings Methanol content disturbances Energy savings Ethanol content disturbances Energy savings

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4.10

Economic Implications of MPC vs the Proposed Control Scheme

So far in this manuscript, we have compared MPC with proposed control scheme in terms of controller performance. But this comparison does not cover the underlying economic implications that inevitably would decide which control scheme would be implemented in an industrial situation. Operating MPC over operating the proposed control scheme allows energy savings of ∼ 400 kW, even during steady periods of plant operations. In general, the plant is likely to operate at steady state for 90 % of the time. In the other 10 %, the plant might experience process disturbances, in this instance the MPC would save ∼ 550 kW.

4.10.1

Monetizing Energy Savings

Assuming the plant operates throughout the year, we can calculate the amount of energy we can save (ESaving ) by implementing MPC. ESaving = (0.4 MW · 0.9 + 0.55 MW · 0.1) · 8300

MWh h = 3445 a a

(4.6)

Before converting the calculated energy savings into saved costs we need to consider the following factors: • Does the plant have an internal need for extra steam? • Can the plant reduce the gas usage? (Many methanol plants require some auxiliary natural gas to supplement the steam generated by waste heat recovered at the reforming process). • Can the plant sell this energy to the electricity market? (By converting excess steam to electricity in a steam turbine-generator). • Can the excess steam be used to process more methanol? Taking these options into consideration, we can come up with two extreme scenarios. In the first scenario, the steam saved can be used to make more product methanol, this is the most profitable scenario. In contrast, the worst case scenario

112


can be where the plant generates all its steam from waste heat and the savings in steam cannot be converted into additional revenue. All practical scenarios fall in between these two extremes.

4.10.2

Cost of Implementation and Other Considerations

When MPC is chosen as a control scheme, the following additional costs need to be considered: • Cost of equipment • Cost of setting up • Cost of maintenance In some cases, the net benefit provided by MPC would be “wiped out” by the cost of implementation and maintenance. Again, the net cost per column will be reduced based on the size of the methanol producer, as implementation of MPC on multiple (similar) columns would reduce set up and maintenance costs. Additionally, we also need to consider the following factors: • General resistance of plant operations to implement MPC • Implications of operating MPC during major process upsets/catastrophic events In general MPC would be favoured over the proposed control scheme during times of high energy costs, not withstanding the above.

4.11

Conclusions

In this manuscript we have developed a novel, industry friendly control structure that is capable of operating an industrial methanol distillation column at high product recovery, whilst maintaining a tight product ethanol specifications and minimising energy usage. To facilitate the development and the testing process a


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validated process simulation of an industrial methanol distillation column was employed. A MPC with a discrete model was also developed for comparison purposes. Both the proposed control scheme and the MPC were subjected to a multitude of disturbance tests, that included disturbance step tests, cyclical feed fluctuations and set point changes. In all tests, the proposed control scheme and MPC were able to maintain both recovery and XD specifications using similar levels of reboiler duty. As such, both control schemes can be potentially implemented in this application. In addition, it was observed that MPC was able to control to the XD specification tighter than the proposed control scheme. This enabled the XD set point to be set closer to the 10 ppm industrial AA grade methanol limit, allowing the average energy usage of MPC to be reduced by ∼ 500 kW, which is ∼ 0.5 % of total reboiler duty. An economic analysis of the proposed control scheme and MPC illustrated that many other factors, such as the value of energy savings and the costs of implementing MPC, need to be considered in deciding between the two controllers. In general however,we found that plants with low value for reboiler duty (steam) savings would prefer proposed control scheme over MPC.

Chapter 5 Economic Performance Analysis 5.1

Prologue

In order to implement any of the suggested control structures in this thesis, it is necessary to clearly illustrate that their implementation would provide an economic benefit to the plant. Plant management agrees that, during day to day operations the control structures proposed in Chapter 3 & 4 would out preform the current control strategy of using excess energy for category 1 and 2 plants respectively (refer to Section 1.3.2)). However, the plant management argues that any gains in day-to-day operations would then be wiped out during non-routine process disturbances. In this Chapter layer of protection analysis (LOPA) have been combined with Net Present Value (NPV) calculations to create a new method for analysing the overall financial benefits of controls. This combined framework allows for the quantification of economic benefits both during normal day-to- day operations as well as non-routine process upsets. As an example, we have compared high recovery control schemes developed in Chapter 4 with the current control strategy and demonstrated that there is a significant financial incentive to implement the proposed high recovery control structures.

5.2

Abstract

In the chemical industry, control strategies which use optimisation to improve the day-to-day performance of unit operations can potentially perform badly during non-routine process upsets. Traditional controller analysis techniques that attempt to quantify the economic benefits of these controller strategies are excellent 115

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Chapter 5. Economic Performance Analysis

at quantifying the day to day improvements, but are ill-equipped to capture and quantify the economics of non-routine process upsets. On the other hand, risk analyses such as LOPA (Layer of Protection Analysis), are excellent at quantifying the economic risks of non-routine process upsets, but are unable to capture the economics of day to day operations. In many plants, the trade-off between optimization vs non-routine operational stability is ultimately settled by experienced plant managers and operators using their best judgement. In this work, we have proposed a combined frame work that combines LOPA with NPV (Net Present Value) analysis that will take the “guess work” out of this type of decision. We have then applied this comprehensive framework to a case study of an industrial methanol distillation column. We compared the current operating strategy used by plant management to a new steady state control strategy that can improve its day to day performance. This comprehensive framework was able to determine that, the proposed control strategy has a better combined overall financial performance than the current control strategy.

5.3

Introduction

Determining the best operating point for key unit operations in process engineering plants is an age-old problem. A plant’s process engineers tend to favour operating points that are close to the steady state optimum, where the plant can reach its highest efficiency or the highest production. The process engineers will, quite correctly, point out that “operating at the steady state optimum will increase the total revenue of the facility”. However, the plant operations engineers will argue that “operating close to steady state optimality will put the plant at risk because we will have no room to deal with process fluctuations ”. This is especially true for plants that have been de-bottlenecked to operate well above original design. They might also add: “Operating at this optimal point will make us more sensitive to fluctuations”. Plant managers generally take these views and their own experience into consideration when making a decision about the unit’s operating point. Plant managers or control engineers will have to make a similar judgement when deciding to invest in new control schemes that promise increased efficiency or capacity. In this paper, we write a general template for such a decision making process by looking at a case study of an industrial methanol distillation column, where adding new sensors and controls can help with improving overall column recovery. We have modified the Layer of Protection Analysis (LOPA) method so that we can


117

look into the differences between two operating points via carrying out a financial analysis. We then extended the financial analysis of LOPA to a comprehensive net present value (NPV) analysis to enable us to make an informed decision about the new controls.

5.4

Background

The implementation of new or advanced controls on unit operations allows them to be operated closer to process constraints. This would inevitably improve the overall economic performance of industrial operations. Accurately quantifying the benefits these new/advanced control schemes deliver, help justify the implementation of them to plant management. Since the emergence of automated computer driven process control in the 1960’s, much work has been done on quantifying its benefits; this quantification process is commonly referred to as Controller Performance Analysis (CPA). In [Camacho & Alba (2013b)], the author talks about the costs and benefits of fitting new “digital devices” to replace the old pneumatic and analog controls that were widely used in the 60’s. In [Jelali (2006)], the author lists different controller performance analysis techniques that are at our disposal. Most of these techniques quantify controller performance by analysing its response to a disturbance or a set point change. In [Yu et al. (2010), Harris & Yu (2007)], authors have argued that even a simple industrial control loop can be non-linear and have proposed methods to analyse the controller performance for these types of systems. The information provided by these CPA tools is valuable as it will allow control engineers to carryout changes to tuning and, perhaps, choose different controller pairings. However, the information generated cannot be easily translated into economic variables such as net revenue, throughput, energy per unit produced etc. In contrast, some frameworks deal specifically with quantifying the economic benefits of control [Davison (1970), Mascio and Barton (2001)]. In this work, the author has created a framework which provides statistically significant results that can prove an advanced controller is doing better than another controller. This is an important aspect in convincing industry of the benefits advanced controls bring versus simple controls that are already implemented in industry [Davison (1970), Honeywell (2011))]. Work into quantifying the economic benefits of control has been conducted for decades. A long term comprehensive assessment of benefits from process controls

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in Australia was conducted by the Warren centre at the University of Sydney [Marlin et al. (1991)]. The study discusses methods to analyse the benefits of new/modified controllers, the ’yard sticks’ which are most appropriate to quantify these benefits and the difficulties of accurately quantifying these benefits. The study also talks about disturbances affecting control systems and how better controls will allow a unit/process to be operated closer to constraints, leading to better performance [Marlin et al. (1991)]. In [Lear et al. (1996)], a methodology that can quantify the costs that disturbances and model uncertainty have on process control is introduced. This work [Lear et al. (1996)] also illustrates the reasons for operating a unit operation at a sub-optimal steady state point when disturbances and other uncertainties are present. In [Contreras-Dordelly & Marlin (2000)], authors present a methodology to determine the optimal operating point to achieve on-specification operations and maintain high profit in normal operations. In [Bauer & Craig (2008)], the authors have carried out an industrial survey on the benefits of using advanced process controls (APC). They found out that most APC suppliers and users carry out an economic benefits analysis that is “rudimentary and based on experience rather than on objective comparison” [Bauer & Craig (2008)]. The survey also shows the importance of ongoing APC application maintenance, which also needs to be factored into the cost of control [Bauer & Craig (2008)]. There have also been industry observations that APC applications often fail due to lack of support, lack of resources and lack of operator training [Honeywell (2011))]. It is also important to note that, for this particular methanol distillation column, we have already proposed a control scheme based on a PID based control loop that carries out a similar function to APC, but without the drawback of needing expert control engineers to maintain [Camacho & Alba (2013b)]. As mentioned previously, there is a large body of work on CPA. Some of these works turn out strictly technical outputs while some of them link the controller’s performance to an economic variable. However, most CPAs do not take into account how changes in day to day operations might impact the unit performance during a major non-routine process disturbance. In our study, to further clarify this point, we look at the operations of an industrial methanol distillation column.

5.4.1

Case Study Background

In high purity methanol distillation, product methanol with < 10 ppm ethanol is manufactured from a feed contains 80% methanol, 20% water and approximately


119

150 ppm of ethanol. With current controls, the column can be operated at 97.5% product recovery (i.e. 97.5% of the methanol leaves as product). An economic analysis shows that if the column can be pushed to operate at 99.5% recovery, there will be a notable positive impact on the industrial methanol producer’s bottom line. In [Camacho & Alba (2013b)], we have designed, tested and recommended a control scheme that can safely operate the column in day to day operations. Based on the current CPA tools, this would register as a positive step and would be welcome by management. However, in industry, the plant managers and operators are rightly worried about how the refining column and the control scheme will react to major process disturbances (disturbances that are not part of the day-to-day variations experienced by the control scheme). In general, operating at 99.5% recovery will make the column more susceptible to major process disturbances and a disturbance of similar magnitude will have a greater influence on the 99.5% recovery column than a 97.5% recovery column. As such, the operators and management will question the economic usefulness of implementing a new control scheme, as any gains made during the steady state operation can easily be wiped out during a major process disturbance. Based on these considerations, it is apparent that, comprehensively assessing the usefulness of the control scheme developed in Chapter 3 & 4: • The increase/ decrease of net revenue during normal operations • The increase/ decrease of net revenue during major process disturbances • The cost of implementing the advanced control scheme Based on our experience, these types of situations are common to many industries. In key unit operations, like refining columns, the decision to proceed or not to proceed are made by senior managers, engineers and operators using their cumulative experience. In most cases, a decision will be made to couple the advanced/ new control scheme with a existing backup control scheme to provide added protection. In this paper we will develop a framework that can systematically capture the overall economic benefit and aid in the overall decision making process.

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5.5


Initial Analysis

To carry out a comprehensive economic analysis, we first need to quantify the cost/benefit of normal operations, major process disturbances and cost of advanced control. This can be carried out by following the general guide lines found in sections 5.5.1, 5.5.2 & 5.5.3 . Once completed, all this information can be further processed to generate a Net Present Value of implementing advanced control on a unit operation.

5.5.1

Day to Day Operations

As mentioned previously, we need to quantify the change in revenue during day to day operations. This can be accomplished by utilizing the concept of marginal costs versus marginal benefits, we can calculate net revenue using Equation 5.1,

∆P rof itss = ∆Rev − ∆R.M

(5.1)

where ∆P rof itss refers to the change in net profits during steady state (normal) operations. While ∆Rev., and ∆R.M refers to the change in net revenue and net costs respectively. Both revenue and raw materials costs need to be expressed on a monetary basis and can be calculated based on economic and plant performance data. To carry out this step, an understanding of the unit operation is important, as changes to production or quality variables can have cascade effects, that need to be traced back to raw materials ( mass inputs and energy to the plant).

5.5.2

Major Process Disturbances

Assessing the impact of major process disturbances on profit requires the following factors to be considered: • The normal operating boundaries of the process (based on day to day variations)


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• All major process disturbances that will take the system beyond the day to day operating boundaries needs to be identified. • The estimated probability of each major process disturbance (incidents per year). • The economic impact of each major process disturbance. • The effect of each disturbance on profits, calculated using the economic impacts and probabilities mentioned previously. Section 5.6 will look at quantifying the economic consequences of these major process disturbances in detail.

5.5.3

Cost of Advanced Control

We also need to estimate the costs of implementing advanced control. For this, we need to consider the following factors: • Implementing additional sensors • Annual maintenance and calibration (above current costs) • Additional back up control or alterations to current back up control • Additional operator training • Implementing the new control architecture and equipment (e.g, a MPC module) All of the costs above can be reasonably estimated in industry and, if a control upgrade project is planned, these numbers will be rigorously calculated in a feasibility study stage of a project . All costs that are one-off costs, such as sensor upgrades and operator training simulations, can be considered as capital costs. However, this framework can also be used as an exploratory analysis tool, where the cost of control can be ignored from the economic analysis. In this instance, we can calculate the economic benefit of installing an advanced control scheme and assess if the benefits are large enough for a more detailed study.

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5.6


LOPA Analysis

To carry out this task, we can adapt the Layer of Protection Analysis (LOPA) framework. Despite being designed to carry out plant risk assessments due to major catastrophic events and other strictly safety related mishaps, the LOPA framework is ideally suited to identify and then quantify the effect of process disturbances. In addition, LOPA analysis will provide help with designing Independent Layers of Protection (ILP) which will act as back up control schemes. LOPA is a simplified method of risk assessment that uses information gathered during a process, such as Process Hazard Analysis (PHA). As with other hazard analysis methods, LOPA has been developed to identify if sufficient layers of protection are present to safeguard against accidents. LOPA is limited to investigating single cause and consequence pairs. LOPA carries out this task by mathematically assessing the likelihood of an initiating event (e.g. process upset) and seeing if there are adequate independent layers of protection (e.g an event that lifts a pressure safety valve) to reduce the probability of the consequences of such an initiating event to an acceptable risk level. In general, LOPA is used to carry out a risk assessment where the consequences are safety related [American Institute of Chemical Engineer, (2001)]. The LOPA framework is gaining popularity in the process industry as it enables the industry to quantify the risks posed. The framework allows the industry to assess the existing or proposed safety strategies and carry out remedial work [Honeywell (2011))]. In general, the following steps need to be considered during a LOPA: • Identify consequences to scenarios • Select a single scenario to analyse further • Identify the initiating event of each scenario and the frequency of such an initiating • Estimate the risk of the scenario happening • Evaluating the risks concerning scenarios


5.6.1

123

Identify Consequences to Scenarios

This is usually done by picking a scenario investigated in PHA and identifying the consequences of such a scenario occurring (e.g what will happen if we accidentally release agent X into the atmosphere). In general, consequences to workers, plant and environment are considered [American Institute of Chemical Engineer, (2001), Center for Chemical Process Safety (2013)]. The methods range from a categorization approach to a qualitative approach and onwards to a quantitative approach. In the proposed assessment, this step identifies the main disturbances that can have an economic effect on the unit operation, such as reducing the rate of production or making off specification products. These disturbances need to be: • Rare enough so they are not day to day disturbances • Large enough to take the unit out of “normal operations” (“normal operations” needs to be defined of a plant to plant basis) • Small enough so it does not trigger a safety system (as this event would be related to a plant wide problem that will require safety systems and operator intervention) It is assumed that a small disturbance will be “ironed out” by standard control schemes, as shown in [Camacho & Alba (2013b)], and would not affect the overall economics of operation. In case a large disturbance or a catastrophic event hits the unit, the safety system will take over to shut the system down or operators would intervene. This type of event will cause all operations to be shut-down or operated at a “safe state”. In addition, if the operators are operating the unit at different steady states, such as higher recovery or tighter specification, the magnitude of the consequence for each scenario can be different for the current versus proposed operations. It is also possible that a scenario has no consequence in current operations whilst still having some consequence at the proposed operations point.

5.6.2

Select a Single Scenario to Analyse Further

A scenario from step 5.6.1 is then further taken into consideration. It is important to note that LOPA will only examine one “cause” and one “effect” at a time.

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If required, multiple permutations of this cause and effect can be studied (e.g a tube “leaking” versus tube “blown off”) [American Institute of Chemical Engineer, (2001), Center for Chemical Process Safety (2013)]. In our control assessment, this step would be the same as a standard LOPA

5.6.3

Identify the Initiating Event of Each Scenario and the Frequency

This task identifies the root cause of the scenario being investigated. The frequency of the event should be based on the likelihood of such an event occurring during normal operations. It is important to note that this step itself carries some uncertainty, as the likelihood of certain events has to be estimated and can potentially be incorrect. As such, care should be taken in estimating these values [American Institute of Chemical Engineer, (2001), Aven & Zio (2011)]. Some companies have published guidelines on how to calculate these values. However, it is suggested that each member of the LOPA team should come up with an answer to the frequency of an initiating event based on their experience/intuition [Dowell (1997)].In our assessment, we will define an initiating cause that takes the system out of normal operations. If the proposed/advanced control scheme operates the column at a different steady state such as a higher rate of recovery or tighter specification we can have a situation where the frequency at which an initiating event occurs increase . As a result the same initiating event can have a different frequency for current operations and proposed operations.

5.6.4

Identify Independent Layers of Protection and Quantify the Probability of Failure on Demand

In this step, ILP’s will be identified that can prevent an initiating event from propagating. Once identified, each ILP’s probability of failure when required to act, referred to as the Probability of Failure on Demand (PFD), will be estimated. ILP by definition must function “independently” and must not be influenced by the initiating cause [Center for Chemical Process Safety (2013)]. The Probability of Failure on Demand (PFD) can be difficult to accurately estimate. Failure to accurately quantify the PFD value will lead to in-accurate results and can potentially lead to bad management/safety decisions. In some


125

corporations, there are internal guidelines that estimate acceptable PFD ranges for different types of ILP’s. Some institutions and professional bodies also publish general guidelines that can be useful in accurately quantifying the PFD for a type ILP [American Institute of Chemical Engineer, (2001), Dowell (1997)]. However, even with this information, accurately quantifying a PFD can be difficult. Therefore, engineers should choose the worst PFD available to negate any complications from under estimating a PFD. In [Dowell (1997)], authors investigated the uncertainty of estimating PFD’s for offshore industries, the potential impact it can have on decision making and possible methods to improve these estimates. In our assessment method, this step can be used to assess whether the control schemes designed to handle non-routine disturbances and other layers of protection, such as plant operators, are truly independent. We can also use this step to identify if operating at an optimized state reduces the “reaction window” of operators which, in turn, can change the PFD.

5.6.5

Estimate the Risk of the Scenario Happening by Combining Steps 5.6.3 & 5.6.4

In this step, equation 5.2 can be used to estimate the risk (probability) of a failure occurring, even with the PFDs in place. It is also important to note that the ILP should be able to function without getting influenced by the initiating cause of failure [Center for Chemical Process Safety (2013)].

Pf aliure = Pinitiation × P F D1 × P F D2 × P F D3 . . . P F Dn

(5.2)

This risk of failure per annum (Pf aliure )can be calculated by multiplying the likelihood of an initiating scenario of happening per annum (Pinitiation ) with the PFD of ILP’s that are present to prevent the initiating event from propagating (P F D1...n ), as shown in equation 5.2 . In our assessment method, this step would be the same as standard LOPA, however, both the frequency of initiating causes, as well as the ILPs present and their PFDs, can be different for each operating point

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5.6.6


Evaluating the Risks Concerning Scenarios

In a classic LOPA, the analysis will be concluded by comparing the outcome from step 5.6.5 to what is deemed tolerable. To reduce the ambiguity of this comparison process, most companies would have their own acceptable risk levels for general scenarios [American Institute of Chemical Engineer, (2001)]. In our assessment method, however, this last step needs to be altered and extended. Rather than comparing the risk of each scenario to the tolerable risk, we need to calculate the total economic loss due to each scenario happening. As a part of this extension, we need to quantify the economic loss that is incurred by a unit operation, even if an ILP deploys and successfully stops the propagation of an initiating event. In a classic LOPA, this is unnecessary as the costs of ILPs not deploying is many orders of magnitude bigger than any losses incurred if ILPs are deployed. It is important to note that LOPA relies on the accurate analysis and quantification of a few key factors which are estimated. Unlike mass or energy balances, these factors cannot be calculated based on a “standard recipe”. LOPA teams will need to use their discretion and experience in: • Potential harm a scenario can cause • Identifying if a system is truly an ILP • Quantifying the PFD • Evaluating what is an acceptable level of “harm” LOPA provides a good framework that can be used to methodically tackle this type of economic issues. In recent years, much academic and industrial work has been done on improving the accuracy of the estimates required to carry out LOPA. In [Schmidt (2014)], the authors looked at the role humans plays as initiators, victims and as an ILP with respect to LOPA. In [Adamski (2006)], authors showed how the PFD of a human (operator) will change based on the “buffer” time they have to react to a situation. In [Khalil et al. (2012)], the authors quantified the range of the PFD of operators to be between 1 (always fails) to 0.001 (fails once every 100 times). In [Ramirez-Marengo et at. (2013)], the authors developed a simplified Fuzzy-LOPA model in estimating costs of scenarios and to estimate PFD


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values for ILP. Meanwhile, in [Johnson (2010)], the authors propose a method to reduce the total cost of equipment required to manage the risks based on economics and risk tolerance criteria. In general, LOPA uses a risk matrix to analyse the severity of the incident. In [Johnson (2010)], the authors extended LOPA beyond studying compliance and use it as a “true risk assessment tool” that can put incidents in perspective. The authors in [Johnson (2010)] also proposed LOPA as a part of cost benefit analysis where decisions on “how much” needs to be spent are made by comparing the annualized risk vs annualized cost of ILPs.

5.7


Net present value analysis (NPV) is a commonly used financial analysis technique in industry. NPV analysis works by discounting further cash flows (with an appropriate discount rate) so we can compare whether the benefits we receive from an investment is sufficient to justify proceeding forwards. Alternatively, we can modify this analysis to calculate the Internal Rate of Return (IRR), where we increase the discount rate until the net benefit from further cash flows equals the initial investment. NPV analysis is ideally suited for our assessment, as we have to first invest money in implementing an advanced control scheme (and perhaps backup control) to improve the performance of our unit operation, which will then provide additional returns in the years to come. In our comprehensive framework, we can take information from step 5.6.5 and information from section 5.5 to carryout a comprehensive financial analysis, that can then be used to make informed investment decisions.

5.7.1

Annual Profit

Before calculating the NPV, we first need to calculate the annual profits/losses a new control strategy would incur during day to day and non-routine process operations. Equation 5.3 can be used for this purpose:

∆P rof itoverall = ∆P rof itss + ∆P rof itnr − ∆M C

(5.3)

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Equation 5.3 calculates the annual benefits (∆P rof itoverall ) by adding changes in profit made during the steady state day to day operations (∆P rof itss ) with the change in profits during non-routine disturbances operations (∆P rof itnr ). Finally, the changes in maintenance cost (∆M C) due to the installation of advanced controls needs to subtracted.

5.7.2

Discount the Annual Net Benefits

To calculate the cumulative annual change in profit, we need to decide on a discounted rate of return and a payback period. In larger corporations, there is a standard payback period and discount rate that is specified by the finance department which is to be used on all projects with a capital expenditure. In some instances, a graduating scale of discount rates can also be used as this better reflects the current market value of the asset. Once the discount rate and payback periods are finalized, a cumulative discount factor (CDF) can be calculated. The cumulative discount is a single value that incorporates both the payback period and discount rate. As such, the annual profit only needs to be multiplied by CDF to calculate the cumulate benefit of the change in annual profit

5.7.3

Calculating NPV

After calculating the CDF, the NPV can be calculated by using equation 5.4.

N P V = ∆P rof itoverall × CDF − CE

(5.4)

In equation 5.4, the NPV of the project is calculated by multiplying the annual benefits (∆P rof itoverall ) with the CDF and then subtracting any capital expenditure (CE) required for implementing the controls, which is calculated based on the method outlined in 5.5.3. If the NPV value is positive, the economics dictate that the project will make an economic profit and should possibly be undertaken. Project proposals with large, positive NPVs are more likely to attract the attention of management. The NPV analysis and the proposed framework can then be used to convince management into investing in advanced control schemes. The proposed framework also allows

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parties that are involved in the process control project to quantify the value of their work, especially in situations where an advanced control scheme may have little effect on normal operations, but improves unit performance during major disturbances.

5.8

Case Study

In order to see the proposed framework in practice, we carry out a detailed analysis of the case study introduced in section 5.4.1.

5.8.1

Normal Operations

Before beginning any detailed calculations, we need to first establish the base operating case for this methanol distillation column. Figure 5.1 and Table 5.1 shows normal day to day column operations and controllers used.

Condenser Cooling Water PC

01 QC

LC

03

02

Distillate

Reflux

FC

Feed

04

Fusel

Refining Column Reboiler LC

05

Bottoms Steam

Figure 5.1: Simplified flow diagram of the high-purity methanol distillation column

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As briefly described in section 5.4.1, even in current operations, the operators have no choice but to operate the column very tightly. Firstly, they have to ensure that less than 10 ppm product ethanol (XD ) specification is maintained, while simultaneously ensuring the bottoms methanol (XB ) does not exceed 10 ppm for long durations. On top of that, operators also passively monitor the product recovery rate which should be kept at high levels. To achieve these tight specifications, the designers of the column introduced a side draw (fusel draw) that extracts most of the ethanol in the feed. These factors all contribute to a unique ethanol profile along the column that forms a bulge near the fusel draw making control difficult. The plant operator’s approach to these issues has been to ignore the complex dynamics of the ethanol bulge and operate the column at a “safe state” using excess reboiler duty. This excess use of reboiler duty produces 5 ppm XD and a 97.5 % recovery rate. Table 5.1: Current operating values

Feed

Distillate

Fusel

Bottom

Specifications

Value Units kg Mass Flow 142500 h Methanol Content 80 wt-% Water Content 20 wt-% Ethanol Content 150 ppm kg Mass Flow 115000 h Methanol Content 99.99 wt-% Water Content 0.1 ppm Ethanol Content 5 ppm Value Units kg Mass Flow 3015 h Methanol Content 84 wt-% Water Content 16 wt-% Ethanol Content 0.61 wt-% kg Mass Flow 25485 h Methanol Content 5 ppm Water Content 100 wt-% Ethanol Content 0 ppm Reflux Ratio 1.687 Reboiler Duty 89.5 MW Methanol Recovery 97.5 %

In Chapter 4, we have proposed a robust control structure that can operate at a recovery rate of 99.5 % and have a XD set point around 9 ppm without breaking the 10 ppm limit during normal day to day process disturbances. However, operating close to the 10ppm limit will likely increase the risk of the proposed controller breaking specification during large process disturbances.


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The use of the proposed robust control structure in hapter 4 (or any other suitable control structure) will increase recovery by 2 T/hr at no extra feed or energy costs (due to only marginal differences in energy usage). However, a detailed look into plant operations shows that the methanol in the fusel draw gets recycled back to the reformer, which in turn, reduces the natural gas requirement. Based on plant data, we estimate that for every 1 T of methanol absent in the fusel draw we will need an extra 19 GJ of natural gas. For the purposes of this comparison, we will assume 1 T of methanol has a value of 330 USD and that 1 GJ of natural gas has a value of 5 USD. These financial values were based on current market values at the time this analysis was conducted. We can now use equation 5.1 (presented in section 5.5.1) to calculate the change in net profit during day to day operations:

∆P rof itS.S = 2 T/hr × 8460 hr/p.a × 330 USD/T − 38 GJ/hr × 8460 hr/p.a × 5 USD/GJ = 3.9 MillionUSD

(5.5)

The significant positive increase in profit justifies further detailed analysis of this case study as any potential negative effects of operating at a higher risk profile will likely be erased by the increase in profit during day to day operations.

5.9 5.9.1

LOPA of the Case Study Identifying an Initiating Cause

Prior to the beginning of a LOPA study, a preliminary study (which can be based on a PHA) should be undertaken to list factors that would have an “economic” effect on the column without posing a safety risk (as described in section 5.6.1). Any initiating scenario that has a safety risk aspect (i.e risk to personnel, environment or equipment) should be dealt with separately. Special attention should be placed on variables or factors that might have a different risk profile for different steady state operating points. In this case, we will focus on an unreliable steam supply.

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5.9.2


Quantifying the Impact of an Initiating Cause

To quantify the impact of an initiating cause we can employ the methods outlined in section 5.6.3 as a basis. In methanol production, steam is generated by recovering waste heat from the reforming process. This steam is then used to supply reboiler duty (heat) to methanol distillation units, as well as to operate turbines and other equipment. Since steam is generated by heat recovery, there can be fluctuations in the steam loop. If there is excess steam available, this can be remedied by releasing some steam to the atmosphere. In low steam situations, controlled auxiliary firing systems can supplement the steam usage. This can only be used for minor fluctuations, however; during major fluctuations, this is not possible. In current operations, the operators have some insurance against this type of a steam disturbance for a limited amount of time as they are operating at a “safe mode” and are, hence, using excess reboiler duty (more that the required amount of steam) during normal operating periods. As such, during some of these disturbances, the operators can still operate the column within the XD and XB specifications. However, even with these safe guards, the above mentioned specifications will be broken a few times a year (approximately twice). As a result of this type of event, the distillation column operates at reduced rates of 50 % for about 12 hours (1 shift) and at an average of 75 % of capacity for another 12 hours. It is important to note that this is a bit of an overreaction as the column can be can be quickly restored (within a few hours) back to full rates. However, both plant operators and management would argue that making on spec XD is more important than reduced distillation rates. The reduced distillation rate creates a potential backlog of crude methanol. In many plants, the distillation columns are designed with 10% extra capacity and can clear this backlog over the course of a week. Operating at 10% excess capacity, however, will require about 10% extra auxiliary firing”. If the plant is operated at a recovery of 99.5 %, the frequency of events will increase to 4-5 times a year (based on an operations and management estimate). With conventional operator intervention, we expect them to follow a similar procedure to current operations. However, with a back up control structure, we can reduce the consequences of each event, such that the operating rates are only reduced of 75% of normal operation for about 6 hours. In all cases, if the back up


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systems fail the plant will have to shut down. In this instance, there will be a loss of production which is equivalent to 72 hours of shut down.

5.9.3

Probability of Propagation and the Economics Risks Associated

This section draws information from sections 5.6.4 & 5.6.5. In this section, we assessed the ILPs present for three scenarios: • Current operating strategy with operators as only back up (Current Strategy) • Proposed operating strategy with operators as only back up (Proposed Strategy) • Proposed operating strategy wwith both a back up control scheme and operators acting as back ups (Proposed Strategy with backup)

Current strategy Since we are operating at a state of over-reflux, a disturbance in steam flow will take more time to push XD out of specification. This allows the operators a 2025 minute “buffer” time without being prompted to make on-the-spot decisions. Based on the operator PFD data presented in [Adamski (2006)] we estimate the PFD for the current strategy is 0.05 per event. Based on this information, the risk of this scenario happening can be calculated as follows:

Risk of scenario happening = 2 events/year × 0.05 faliures/eventRisk of scenario happening = 0.1 faliures/year

(5.6)

Proposed strategy As mentioned above, the proposed control strategy will attempt to operate the distillation column at a methanol recovery of 99.5%. Since the reaction time is

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reduced due to normal relux rates, we estimate a PFD of 0.1 based on the operator PFD data presented in [Adamski (2006)]. Based on this information, the risk of this scenario happening can be calculated as follows:

Pf aliure = 4.5 events/year × 0.1 faliures/event = 0.45 faliures/year

(5.7)

Proposed strategy with backup In this scenario, we have an additional back up control structure that will help be the first line of defence. As such, we have two ILPs that can counter a steam flow disturbance. The PFD of a control loop is estimated to be 0.01 per each event [Adamski (2006)]. The PFD of the operators will still remain at 0.1 per event. Based on this information, the risk of this scenario happening can be calculated as follows:

Pf aliure = 4.5 events/year × 0.01 ILP1 faliures/event × 0.1 ILP2 faliures/event = 0.045 faliures/year

5.10

(5.8)

Cost vs. Benefits

In this section we will use the methods outlined in section 5.7 to compile the overall financial performance of the column taking into account both steady state and non-routine operations . To calculate the net benefits, we first have to quantify the annual benefits of each scheme. We will use the current control strategy as the base case and calculate the net benefits for the proposed control strategy with and with out back up control. To carry out the economic analysis, we will make the following estimates and simplifications: Reboiler duty requirement Net Revenue from Production Average Production rate

2.7 120 115

GJ/T of MeOH

USD/T of MeOH

T of MeOH/hr


5.10.1

135

Proposed Control Scheme (without backup) versus. Current Strategy

In this section we will calculate the economic benefit (NPV) of operating the column using the proposed control scheme without a back up control scheme versus using the the current control strategy. In Equation 5.5 we have already quantified the steady state economic benefit of using the proposed control scheme (without backup) as opposed to using the current control strategy. We will now look at the economics of these two control strategies during non routine operations. We have divided non-routine operations into two categories. Firstly, we will quantity the costs associated with a situation where ILP’s have deployed and secondly we will quantly the costs associated with a situations where all ILP’s have failed. Cost of ILP deployment To calculate the costs associated we first have to calculate the probability of ILP deployment (PD) for the current strategy (cs) and the proposed control scheme (ps).

P Dcs = 2 events/year − 0.1 faliures/year = 1.9 deployments/year

(5.9)

P Dps = 4.5 events/year − 0.45 faliures/year = 4.05 deployments/year

(5.10)

Since both strategies have the same costs associated with when they are deployed, we can calculate the change in costs of implementing the proposed strategy during ILP deployment (∆CostsDip ), by quantifying the accumulation of crude methanol during the deployment period (M eOHacc ), and the subsequent requirement of excess energy to refine this back log. Based on information accumulated in sections 5.8.1 & 5.9.2, This is as follows:

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M eOHacc = 115 T/hr × 50 % × 12 hrs + 115 T/hr × 25 % × 12 hrs = 1035 T/Deployment

(5.11)

∆CostsDip = (4.05 − 1.9)(×)1035 T × 2.7 Gj/T × 5 USD/GJ = 13972 USD/year

(5.12)

Cost of ILP failure To calculate the cost associated with failure of all ILP’s (∆CostsF ail ),We first need to calculate the cost of failure by quantifying the loss of methanol production (∆P rod), and the loss of revenue associated with this loss. We can then used the relevant probability of failure values calculated in Equations 5.6 and 5.7 to calculate the costs.

∆P rod = 115 T/hr × ×72 hrs = 8280 T/faliure

(5.13)

∆CostsF ail = (0.1 − 0.45)(×)8280 T × 120 USD/T = 347760 USD/year

(5.14)

Annual increase in profit To calculate the annual Increase in profit due to the implementation of the proposed control strategy as opposed to operating the column using the current strategy, we need subtract the costs incurred during non routine operations calculated in Equation 5.12 and 5.14 out of the steady state profit made calculated in Equation 5.5. The results are tabulated in Table 5.2. Table 5.2: Annual change in net profit (proposed control (without backup) verses the current control strategy (in USD)

∆P rof itS.S ∆CostsDip ∆CostsF ail ∆P rof itoverall

Annual increase in Profit 3,900,000 -13,972 -347,760 3,540,000


5.10.2

137

Proposed Control Scheme (with backup) versus Current Strategy

In this section we will calculate the economic benefit (NPV) of operating the column using the proposed control scheme with a back up control scheme versus using the the current control strategy. We will now look at the economics of these two control strategies during non routine operations. Cost of ILP deployment First we will calculate the probability of ILP deploying for the current strategy (cs) and the proposed control scheme with backup control (bs):

P Dcs = 2 events/year − 0.1 faliures/year = 1.9 deployments/year

(5.15)

P Dbs = 4.5 events/year − 0.045 faliures/year = 4.45 deployments/year

(5.16)

In this instance, the two strategies have different costs associated with deployment. The methanol accumulation during ILP deployment for the current strategy has already been calculated in Equation 5.11 in the previous section.For the proposed strategy with back up, we need to calculate the crude methanol accumulation during an ILP deployment:

M eOHacc = 115 T/hr × 25 % × 6 hrs = 172.5 T/deployment

(5.17)

Combining the values calculated in Equations 5.11 and 5.17 we can calculate the costs incurred by the proposed control strategy with backup during an ILP deployment:

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∆CostsDip = (4.45 × 172.5 T − 1.9(×)1035 T) × 2.7/GjT × 5 USD/GJ = −16215 USD/year

(5.18)

The negative answer calculated in Equation 5.18 reflects that the proposed control strategy with a back up controller will save money during an ILP deployment. In other words the proposed control strategy has a lower cost associated with ILP deployments with respect to the current control strategy Cost of ILP failure To calculate cost associated with the proposed control with back up during total ILP failure, we will take the loss of methanol during failure calculated in Equation 5.13 and the probability of failure values calculated in Equations 5.8 and 5.6 to directly calculate the costs:

∆CostF ail = (0.045 − 0.1)(×)8280 T × 120 USD/T = −54648 USD/year

(5.19)

The negative answer calculated in Equation 5.19 reflects that the proposed control strategy with a back up controller will save money during an ILP deployment.

Annual change in profit To calculate the annual Increase in profit due to the implementation of the proposed control strategy as opposed to operating the column using the current strategy, we need subtract the costs incurred during non routine operations calculated in Equation 5.18 and 5.19 out of the steady state profit made calculated in Equation 5.5.The results are tabulated in Table 5.3.

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Chapter 5. Economic Performance Analysis Table 5.3: Annual net benefit proposed control scheme (with back up) vs current control Strategy

∆P rof itS.S ∆CostDip ∆CostF ail ∆P rof itoverall

5.10.3

Profit (cost) in USD 3,900,000 16,215 54,648 3,970,000


If we estimate a project payback period of 5 years and a discount rate of 25%, we can calculate the net present value of the proposed control strategy with and without back up control as shown in Table 5.4: Table 5.4: NPV of the two control options

Controls without backup

Controls with backup

∆P rof itS.S

3,900,000

3,900,000

∆CostDip

-13,972

16,215

∆CostF ail

-347,760

54,648

∆P rof itoverall

3,540,000

NPV(5 years @ 25%)

9,420,000

3,970,000 10,670,000

Based on the calculated net present values, we can make the following comments about these two control strategies: • In steady state, both controllers out perform the current operating strategy. • During major process disturbances, the proposed controller without backup performance worse than the current control strategy while the proposed control with backup performance better. • As long as the control strategy for steady state operations costs less than $ 9.42 million (NPV) to install and operate, it should be implemented, and the labour to implement the project • We would estimate the cost of introduction of a new steady state control strategy to cost < $ 2 million. The major part of this cost would include the introduction of on-line analysers to monitor and control the ethanol and methanol profiles in real time.

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• Implementing the back up control together with the proposed control strategy boosts NPV by $ 1.25 million. Therefore, a back up controller should only be installed if it costs < $ 1.25 million to install and operate. • We would estimate the cost of introducing a back up control strategy to cost < $ 0.5 million. The major part of this costs would be in the labour required to set-up and test the back up control schemes. In this particular example, the proposed LOPA analysis enabled us to: • Quantify the economic impact of the adverse effects created by the proposed control strategy. • Calculate the value addition of the back up control. It is also important to note that in some scenarios, this comprehensive analysis of the effects of control becomes even more important. For example, if we assume in steady state the control strategy can only produce an extra 0.5 T/hr of methanol, then the NPV of the proposed control strategy with no back up control is only $ 1.54 million. Even with a simplified control strategy, as proposed in [Camacho & Alba (2013b)], the cost of installation and maintenance might outweigh the calculated NPV value. With a back up controller, the NPV of the proposed control scheme would reach $ 2.79 million and would have a better chance of getting implemented. It is also important to note that the discount rate used in this particular case study can be considered to be high. A discount rate of 15% is the norm for these type of projects but the use of a high discount rate allows for a tougher preliminary calculation and will nullify small capital outflows that might occur during the implementation process (eg: requirement to hire external consultants to train plant operators on safety shut downs and to creates operator training simulations with the altered control set-up) to be nullified.

5.11

Conclusions

In this work, we have identified that some control strategies which improve the day to day performance of unit operations can have a potential negative effect


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during process upsets. We found that traditional controller performance analysis techniques are not equipped to capture the financial benefits of control strategies during day to day operations and during process upsets. In this work we proposed a novel way of carrying out a comprehensive economic analysis that combines LOPA analysis with a NPV analysis to provide a complete economic benefit analysis of a control strategies. We have then applied this combined analysis technique to a case study of an industrial methanol distillation column, where we compared a current operating strategy of the column to a new steady state control strategy and to a strategy that has both a new steady state and back up control. In this analysis, we were able to quantify the NPV of implementing these strategies and was able to capture the economic value of a back up control strategy.

Chapter 6 Conclusions This thesis describes the author’s investigation into how better process control and operational changes can improve the product recovery, stability and energy efficiency of industrial methanol distillation columns. To achieve these objectives six research aims were devised in Chapter 1, these aims focused on: • Operational improvements. • Practical robust control. • Quantifying the value of better control.

6.1

Operational Improvements

A validated steady state process simulation of an industrial methanol distillation unit was developed and used to establish the feasibility of operating the column at a high product recovery (β) of 99.5%. The simulation was then used to carry out operational optimization, where it was concluded that: • Reducing the mass flow rate of the side draw improves the energy efficiency the column. • Lowering the side draw location improves the energy efficiency of the column. • For a given product recovery, there is a combination of reducing mass flow rate and lowering the side draw location that gives the optimal energy efficiency. 143

144

Chapter 6. Conclusions

Based on these findings both side draw location and flow rate were simultaneously changed to find the minimum reboiler duty (energy) required to operate the column at β of both 99.5% and 97.7 %. Initial sensitivity tests carried out during the steady state analysis illustrated that the column is more susceptible to disturbances at higher recovery rates. The investigation also found that reducing the mass flow rate of side draw and lowering the side draw location makes higher β operations easier to achieve. This was the first instance in published literature where the side draw location and flow rate of an industrial methanol distillation column was optimized to improve its energy usage.

6.2

Practical Robust Control

An analysis of the column illustrated that the ethanol profile along the column forms a bulge near the side draw and that this bulge needs to be managed to achieve stable column operations. In chapter 3, the formation and dynamic movement of the ethanol bulge was further examined. Based on this examination it was concluded that: • The ethanol concentration at the side draw exhibits complex time and state varying process dynamics. • If the ethanol concentration at the side draw is used as a process variable, a complex control algorithm is required. • The “complex process dynamic” response of the side draw ethanol concentration is caused due to the movement of the ethanol bulge. • If the movement of the ethanol bulge is kept in the right place and shape, the control of the side draw ethanol concentration can be done using a simple PI controller. With this information, we developed a novel side draw control structure that uses three composition analysers around the side draw region of the column to capture the movement of the ethanol bulge. This information is then used to successfully control the ethanol bulge profile. During disturbance tests the proposed control structure guaranteed on-specification product methanol without the use of excess reboiler duty. A standard DV controller implemented on the column


145

was unable to achieve this objective. The control structure developed in Chapter 3 is suitable for plants where the methanol distillation column forms the “plant bottleneck” (Category 1 plants as defined in section 1.3.2). In many methanol production plants, however, distillation columns have limited crude methanol availability ( Category 2). In these instances, a control scheme needs to recover as much methanol while making on-specification product. The control structure developed in Chapter 3 is ill-equipped to deal with this type of operation. Chapter 4 investigated the possibility of developing control structures that can operate on-specification at high recovery. An initial analysis conducted into high recovery operations concluded that: • The process is highly non-linear at high recoveries. • The average process time constants and dead times are large. • A model predictive controller (MPC) would be ideal for managing recovery while keeping product on-specification. • The use of a MPC can be a difficult implement in industry due to the complex nature of the controller, the cost of implementation and robustness. • A simple, robust process control structure that can match the performance of MPC would be well received in industry and would provide a viable alternative. Based on this information, we also developed a novel, robust control structure that is able to control both the recovery and specification using override logic. For comparison purposes, a MPC was also set up. A comparison between the MPC and the proposed controller found that: • Both MPC and the proposed controller operate at similar product recoveries and product specification, and have similar levels of control during process disturbances. • The MPC uses marginally less reboiler duty. As such, from a purely control point of view, a MPC would be the better choice. • In an industrial environment, the MPC has extra installation and running costs will likely off-set the annual savings in energy.

146

6.3


Value of Better Control

In an industrial setting, an existing control structure (or the day to day plant operation point) would not be changed unless there is a significant financial incentive to do so. All control structures proposed in this PhD thesis alter the day to day plant operations point, especially the controls proposed in Chapter 4. When discussing the implementation of the control structures proposed in Chapter 4, the plant managers and operators agreed that there will be significant financial benefits in day to day operations by adopting the proposed control schemes, but they also believed these benefits would be diminished during non-routine process disturbances. As such, operating at the current state of excess reboiler duty is justified. When analysing this issue further we found that: • Reboiler duty is a non-routine process disturbance that creates off-specification methanol and reduced product recovery rates. • Operating the column at high product recovery rates and closer to the product ethanol specification makes the column more sensitive to non-routine reboiler duty fluctuations. To justify the implementation of the controls proposed in Chapter 4, a comprehensive economic analysis needs to be carried out to explicitly quantify the gains and losses during both day to day operations and non-routine operations to explicitly calculate the overall benefits of the proposed controls. A literature search into this area revealed that there is no comprehensive framework to carry out this type of explicit analysis. However, we adapted and combined the already Layer of Protection Analysis framework (a safety analysis tool) with Net Present Value assessment to establish a framework that can be used to carry out a comprehensive financial analysis. In this analysis we concluded that: • The financial risk profile (probability and consequences) of non-routine reboiler duty fluctuations are higher if the column is operated at high recovery. • The day-to-day financial gains of the proposed control structures significantly outweigh the financial losses accumulated during non-routine reboiler duty fluctuations. • From an overall financial point of view, the proposed operation in Chapter 4 is a financially superior alternative to current operations.


147

• There is some financial incentive to create a backup control structure that will manage the column performance during non-routine reboiler duty fluctuation.

6.4

Future Work

Over the course of this thesis, some interesting topics were found that were either outside its scope or are an extension of the work presented. These are outlined below for researchers who intending to do further work in related areas. • In Chapters 4 & 3 two distinct modes of column operation was studied and two control structure developed. As an immediate extension the conceptual ideologies of these two control structures can be looked at together. • In Chapter 4, a linear MPC was successfully employed to control a methanol distillation column. As an immediate extension of this thesis, we are currently implementing a non-linear MPC for completeness. • The use of divided-wall columns to carry out multi-component separation is promising. We are currently carrying out a feasibility of using this type of column for the next generation of methanol plants. • The dynamic process simulations of industrial methanol distillation units built during the PhD thesis can be used as a case study for exergy based process controls. The outcome of this study can be used to validate the commercial usefulness of exergy based control. • There is currently no work that examines the impact that the design of a methanol distillation unit has on both the capital and operational expenditure. This would be an interesting avenue to study, especially if price fluctuation in chemical and gas markets changes the optimal design solution. • Modelling the control scheme transition between the day to day control structure and the back-up control structure as described in Chapter 5 will be of interest. • Air separation units (ASU) like methanol distillation columns require dual high purity product (nitrogen and oxygen), while the non-key argon needs to be extracted from the middle of the column. Currently ASUs employ a two

148

Chapter 6. Conclusions column configuration to carry out this separation process. We are currently looking into this subject.

Appendix A Design of Experiments Steady State Experimental set-up and specifications. Within the DOE, the higher recovery is achieved without changes in the set up, so only by adjusting the mass flow rates of the distillate and the side draw, as well as the reboiler duty. For the analysis, changes in feed flow M˙ F , reboiler duty Q˙ Reb , methanol XF,M eth and ethanol fraction XF,Eth in the feed are considered. Since the experiment is run at two levels of disturbances, high and low, this results in a 24 factorial design. The values for the disturbances are based on the experience of the real life plant, which are deviations of • 2.5 % in feed flow M˙ F • 5 % in reboiler duty Q˙ Reb • 1 % in methanol fraction XF,M eth of the feed • 50 % in ethanol fraction XF,Eth of the feed. For the execution of the DOE the distillate flow and the flow of the side draw are kept at the same level. As a result, a change in feed flow rate only affects the bottoms flow rate. To balance the changes in XF,M eth and XF,Eth for the different runs, the water content of the feed XF,H2O , is adjusted accordingly. For the DOEs the high and low levels of the input variables are given in Table A.1. For both DOEs the feed flow and composition are the same, but the reboiler duty is higher for enhanced recovery, since an increased reflux needs to be achieved for the separation. 149

150

Appendix A. Design of Experiments Steady State Table A.1: Values of the input variables for both points of operation.

Main effect A M˙ F B97.7 % Q˙ Reb B99.5 % Q˙ Reb C D

XF,M eth XF,Eth

Standard

+

-

141976 86.20 88.53 0.8258 1.50E-04

145525 90.50 92.95 0.8340 2.25E-04

138426 81.89 84.10 0.8175 7.50E-05

As response variables the ethanol XD,Eth and methanol XD,M eth concentration in the distillate, the methanol concentration XB,M eth at the bottom and the recovery Rec are observed. The response variables for the two different modes of operation without any disturbance are displayed in Table A.2. Table A.2: Response variables for standard and increased recovery without disturbance.

a b c d

XD,Eth XB,M eth XD,M eth Rec

7.00E-06 7.26E-06 0.999993 97.7

7.00E-06 6.99E-06 0.999993 99.5

Analysis and acceptance of the results. To judge the significance of the different disturbances, Q-Q plots are generated in MATLAB for the two operation points. The plots can be found in Figure A.1 and Figure A.2. Effects without significance almost behave like a random sample drawn from a normal distribution with zero mean, which implies the plotted effects will lie approximately on a straight line. The effects that deviate from the line are those of concern.

151

Appendix A. Design of Experiments Steady State ·10−4 Quantiles of Effect

XD,Eth

2.0 0.0 −2.0 −4.0 −2 1.0

Quantiles of Effect

1.5

−1 0 1 Normal Quantiles

·10−2 C A

0.5 0.0 −0.5 −1.0 −2

AC −1 0 1 Normal Quantiles

2

A

XB,Meth

1.0 0.5

AC

C

0.0

2.0

XD,Meth

·10−1

−0.5 −2

2

Quantiles of Effect

Quantiles of Effect

4.0


2

·10−2

0.0 −2.0

AC C

−4.0 A −6.0 −2

Rec −1 0 1 Normal Quantiles

2

Figure A.1: Q-Q plots of the effect for 97.7 %

It can be seen in Figure A.1, that the ethanol concentration XD,Eth is barely affected by the disturbances for a recovery of 97.7 %, as the plotted effects almost form a straight line. The other three response variables XB,M eth , XD,M eth and Rec seem to be affected most by the feed flow (A), followed by the methanol concentration in the feed (C). The interaction (AC) of both effects is also distinctive.

152

Appendix A. Design of Experiments Steady State ·10−4

0.0

AD A

−4.0 −2

Quantiles of Effect

3.0

D


Quantiles of Effect

XD,Eth

2.0

−2.0

1.5

·10−2

2.0 1.0

A C

0.0 −1.0 −2

AC −1 0 1 Normal Quantiles

2

A

XB,Meth

1.0 0.5

C

AC

0.0

1.0

XD,Meth

·10−1

−0.5 −2

2

Quantiles of Effect

Quantiles of Effect

4.0


2

·10−2

0.0 −1.0

AC

C

−2.0 A −3.0 −2

Rec −1 0 1 Normal Quantiles

2

Figure A.2: Q-Q plots of the effect for 99.5 %.

Figure A.2 shows, that for a recovery of 99.5 %, the ethanol concentration XD,Eth is affected by disturbances in the feed flow (A) and the ethanol concentration in the feed (D), whereat the effect of the feed flow dominates. The effect of the interaction (AD) is visible, too. XB,M eth , XD,M eth and Rec are again disturbed severely by the feed flow (A) and the methanol concentration in the feed (C), with the interaction (AC) of both. The Q-Q plots demonstrate clearly, that for an operation at increased recovery the variable XD,Eth develops a distinct sensitivity towards changes in feed flow and the ethanol concentration in the feed. The disturbances in feed flow become perceptible, because at 99.5 % recovery the column is operating really close to the absolute methanol recovery, which means almost the entire methanol being fed into the column is contained in the distillate already. As the methanol feed flow drops, while distillate flow and reboiler duty are unchanged, a bigger fraction of the methanol entering the column will be drawn at the top. This can go up to

Appendix A. Design of Experiments Steady State

153

almost 100 % recovery for a methanol feed being lower than the distillate flow. The impurities in the product begin to increase severely, as the lack of methanol is balanced with other components. The higher the recovery, the bigger is the fraction of methanol already drawn at the top of the column and therefore even small disturbances can be sufficient to cause a methanol deficit. An under-supply of methanol can force all the ethanol (as there are only 10 kg/h in the feed) and even parts of the water into the distillate. At this point XD,Eth becomes more sensitive for changes in the ethanol content of the feed, as the changes are passed on to the distillate composition to the full extent. For further analysis, all the effects of the other three variables are displayed in the bar chart in Figure A.3, to allow a convenient comparison between the two operation points for these variables.

154

Appendix A. Design of Experiments Steady State ·10−2

Methanol at the top

Effect

2.0 0.0

−2.0 yMethanol at the bottomy

Effect

0.1 0.0

−0.1 Recovery

A B C D A B AC A D BC BD C A D B A C B ACD BCD A D BC D

Effect

4.0 2.0 0.0 −2.0 −4.0

·10−2

97.7% Recovery

99.5% Recovery

Figure A.3: Effects of the different disturbances on the response variables.

The responses of the variables XB,M eth , XD,M eth and Rec resemble one another for the two different operation points, as already observed in the Q-Q plots. All three show the highest sensitivity towards changes in the feed flow M˙ F , followed by disturbances in the methanol content XF,M eth and the interaction of both disturbances. All the remaining effects on XB,M eth , XD,M eth and Rec are insignificantly small. At a higher recovery, the product purity seems to be affected more by an increase of the feed flow rate or methanol fraction of the feed. The column is operating at a higher boil-up ratio, and the lower side draw flow rate at increased recovery favours the primary separation more. Therefore, the column is able to accumulate a bigger part of the additional methanol in the distillate. As a consequence the recovery is affected less.

Appendix A. Design of Experiments Steady State

155

To understand the directions of the effects the process must be analysed more closely. With an increase in feed flow, more methanol enters the column along with the other components. As more methanol is fed to the column, while the distillate flow is unchanged, the product purity improves. But, with the reboiler duty being steady and the distillate flow being limited, most of the excess methanol is drawn at the bottom. This leads to a higher methanol concentration at the bottom and a lower methanol recovery. As to be seen in Figure A.3 the effects on the bottoms methanol composition are therefore fairly high for both recoveries. Changes in the methanol content of the feed have a similar impact. When the methanol content in the feed increases, the methanol purity of the distillate improves, since the same effort is put in the distillation although the separation facilitates. Here, the distillate rate is also limiting the amount of methanol leaving the column at the top and therefore lowering the recovery and purity of the bottoms.

Appendix B Establishing a method of calculating recovery B.1

Prologue

In Chapter 4 we developed a control structure that used product recovery as a control variable. In this instance the product recovery rate was calculated by monitoring both the mass flow rate and the mass fraction of methanol in both the feed and distillate streams. From an implementation perspective this requires the installation of an extra gas chromatograph and an accurate mass flow meter at the feed stream, while the respective monitoring devises are already available in distillate stream. As such the cost of installing and maintaining the structure suggested in Chapter 4 can be expensive. In this appendix we explore the possibility of measuring recovery rates using more cost effective method.

B.2

Abstract

Efficient operation of industrial methanol distillation columns require the production of high purity methanol at high recovery (β). However, in many distillation units β is not actively monitored as this requires the installation of expensive gas chromatographs and accurate feed mass flow measurements, which can be complicated. In this work we have developed a novel, simple and economical method based on density and flow rate measurements to calculate β of industrial methanol distillation columns. Step and disturbance tests carried out on the simulation suggest the proposed method is able to fairly accurate estimate β. However, The β 157

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Appendix B. Establishing a method of calculating recovery

calculated from the proposed method can deviates from the actual β during dynamic transitions.

B.3

Introduction

The inference of hard to measure process variables such as product concentration by easy to measure process variables such as temperature is a well established practice in process industry [Svrcek et al. (2014)]. In industrial methanol distillation product recovery (β) is a key process variable that needs to be actively monitored and controlled. β of a methanol distillation column can be determined by calculating the ratio of methanol leaving the column as products and the amount of methanol entering at the feed. The monitoring of β is further complicated due to the high levels of accuracy required as small errors associated with inference can create large deviations in the calculated value. One way achieve high accuracy would be to calculate β directly by analysing the methanol concentration and mass flow rates of the feed and distillate flows. Gas chromatograph (GC) have been used since the 1960’s to analyse and quantify the concentrations in process industry [Dettmer-wilde & Engewald (2014)] and can be adopted easily to measure the feed and distillate flow concentrations in industrial methanol distillation units. The main drawback of a GC is the costs associated with installation, utilities costs and maintenance [Handley and Adlard (2001), Taube (2014)]. In the recent years the capital costs of GCs have reduced [Handley and Adlard (2001)], but, using a GC also requires dedicated and very well trained technician(s) to generate accurate results which can be expensive. In some cases more than 70 % of the overall costs of a GC is incurred during operations [Handley and Adlard (2001)]. However, when all other inference methods fail, the plant managers have no other choice than to use a GC to accurately measure concentration [Taube (2014)]. In addition to a GC the direct quantification of β also require the accurate measurement of distillate and feed mass flow rates. A Coriolis flow meter would be an ideal instrument for this measurement as this method provides accurate mass flow measurements required[Webster & Eren (2014)]. Coriolis flow meters are expensive to purchase and requires more maintenance in comparison to other volume flow measurement devices [Webster & Eren (2014), Taube (2014)]. One way of avoiding these relatively costly equipment is to correlate β to


159

another other easier to measure variable(s). For example in high purity distillation columns temperature at the middle part of the column has been correlated to the product specification [Fuentes & Luyben. (1983)]. In [Rogina et al. (2011)] the authors developed a “soft sensor” based on easy to measure temperature, pressure, flow, etc.. variables to monitor the performance of a crude oil distillation unit. In [Raghavan et al. (2011)] the authors also developed a soft sensor to estimate compositions based on a recurring neural network. With advances in process simulators we are now able to accurately model processes and employ them to seek these correlations[Luyben (1964)]. This practice also allows for multiple scenarios and variables to be investigates without putting the real plant at risk [Svrcek et al. (2014)]. In this work we employed a validated process simulation of the industrial methanol distillation column to explore the possibility of using alternative variables to establish an accurate method to calculate β. Based on these guidelines we developed a method that uses density and volume flow rates, as well as a fundamental understanding of the process and modes of operations to accurately quantify β during plant operations.

B.4

Set-up

In methanol refining, a multi-component feed (methanol, water, ppm level of ethanol) is refined to achieve tight high purity product & bottoms (ppm levels) and high recovery rates (≥ 97.5 %). The feed conditions for the high-purity column are given in Table B.1. To produce AA grade methanol the distillate stream needs to have a methanol purity of 99 % and < 10 ppm ethanol impurities. To satisfy waste water treatment restriction the bottoms stream must contain < 10 ppm methanol in the bottoms. To accomplish such a high distillate purity and bottoms restrictions, most of the ethanol needs to be taken out of the column via the side draw. To satisfy both operating stability of the column and limitations on the disposal system, the mass . flow of this fusel stream must be kept within 2600 − 3700 kg h From a commercial point of view it is also important to maximize the profitability of the plant. this requires the methanol recovery in the distillation collumn to

160

Appendix B. Establishing a method of calculating recovery Table B.1: Results of the steady state simulation from [Udugama et al. (2015)]

Feed

Distillate

Fusel

Bottom

Specifications

Value Units kg Mass Flow 142500 h ◦ Temperature 80 C Pressure 170.8 kPa Methanol Content 82.5 wt-% Water Content 17.5 wt-% Ethanol Content 150 ppm kg Mass Flow 116000 h Methanol Content 99.99 wt-% Water Content 0.1 ppm Ethanol Content 8 ppm kg Mass Flow 3015 h Methanol Content 50 wt-% Water Content 50 wt-% Ethanol Content 0.61 wt-% kg Mass Flow 25485 h Methanol Content 5 ppm Water Content 100 wt-% Ethanol Content 0 ppm Reflux Ratio 1.687 Reboiler Duty 89.5 MW Methanol Recovery 99 %

reach a high level (≥ 99 %). where product methanol recovery β of the distillation unit is defined as:

β=

m ˙ M eOH,Dist · 100 % m ˙ M eOH,F eed

(B.1)

In equation B.1 the component mass flow rate of methanol in distillate m ˙ M eOH,Dist is divided by the incoming methanol in the feed stream m ˙ M eOH,F eed . Equation B.1 calculates the instantaneous recovery of the column as it uses the actual refining column feed and distillate information. This recovery however does not consider the process dynamics with in the column. As a result, during a step up in feed flow, the calculated β value will immediately hange . Thus, this equation better reflects the ”external” instantaneous mass balance.


B.5

161

Modelling & validation

Process modelling and simulation is well recognized tool for critical decision making, control design and optimization in the process engineering community [Svrcek et al. (2014)]. In this paper the commercial simulation software HYSYS was used to build a dynamic process model which was then validated against plant data shown in Table B.1. Once validated, the simulation was transitioned to a β of 99.5 % and revalidated based on plant trials and other available data. The vapour liquid equilibrium (VLE) in the distillation column is described by an activity coefficient approach with a property package using the Wilson model. In this method activity coefficients of the mixture are first determined to calculate the fugacity of the liquids, while a virial equation of state was chosen to calculate the vapour fugacity. The column solver uses equilibrium and enthalpy models paired with rigorous thermodynamic calculations to form results. An overall tray efficiency of 80 % is assumed and a maximum flooding factor of 120 % is set. figure B.1 shows a schematic of the distillation column in process simulation

162


Figure B.1: Simulated model with controller used for testing purposes

In order to operate the process model at a stable and steady operating point basic PID controllers to control condenser pressure, fusel flow and reboiler level were introduced. In addition to these controller an ethanol composition controller (XIC) is used to ensure on specification products. The inclusion of the XIC control means any changes or disturbances felt by the column would not effect the product ethanol composition, instead these disturbances would be rejected to the recovery rate.

B.6

Basis of calculation and estimates

For the calculation of a β it’s necessary to know the exact amount of methanol which is entering in the feed and exiting through the product draw, this is the basis of equation B.1. Since we require accuracies of +/- 0.1% β this will require the use of continuous composition (via gas chromatograph) and mass flow measurement


163

(Via a Coriolis meter or other measurements) at these two streams. In industrial methanol distillation units a gas chromatograph and a accurate mass flow meter are already present in the product draw. These measurement devices are required to • Monitor and maintain < 10ppm ethanol AA grade methanol standards (gas chromatograph) • To accurately quantify the amount of methanol produced by the each unit as this information is used in the sales process As such to calculate β based on equation B.1 we only need to quantify the amount of methanol in the feed. If the feed volume flow rate and feed density/temperature is used to calculate the recovery rate, we would no longer have the necessary sensitivity for control (i.e. 2% deviation in feed mass flow = a change in β of 2%). Thus, calculating the β requires a gas chromatograph and an accurate flow meter to the feed stream. This although practical will have noticeable capital and operating expenditure. In this paper we try to remedy this issue by proposing a novel method to calculate the β without knowing the amount of methanol in feed draw. The idea is to calculate the methanol recovery with the density of the side-draw stream (fusel). By using the fusel density, it is possible to significantly reduce complexity and the amount of required sensors and still calculate the β at high accuracy. As a result of reducing the number of sensors, the capital and operating expenditure of quantifying recovery can be also reduced. The β calculation using the fusel density is based on the fact that the density is dependent on the composition of a mixture. Based on plant data and column mass balances it can be established that little (< 5ppm) to no methanol leaves the column through the bottoms draw (during normal operations) if the column is operating at >98% recovery. From the background information is known that the product stream contains >99.99% methanol. Thus, from a methanol mass balance perspective a proportion of 100 % methanol in the product stream can be assumed. With these estimates, equation B.2 is proposed

m ˙ M eOH,Distillate = m ˙ Distillate

(B.2)

164


The density of a solution is depending of the densities and the mass fractions of the pure substances in this solution. Density of the fusel draw can be measured directly with a sensor on the fusel stream. Furthermore, it is assumed that the density of the fusel stream is predominantly affected by water and methanol, which are the two substances in major proportions. Other components are only contained in minor proportions and their effect on the density is negligible. Based on plant data and several simulations, these estimates have been confirmed to be appropriate. Once density is calculated this data can be coupled with the volume flow rate information of the fuse draw calculate it methanol mass flow rate. Based on this information the overall methanol mass balance can be rewritten as follows

B.6.1

m ˙ M eOH,f eed = m ˙ Distillate + V˙ × ρf usel × XM eoh,F usel

(B.3)

m ˙ M eOH,f eed = m ˙ Distillate + m ˙ f usel × XM eoh,F usel

(B.4)

Methanol recovery rate calculation

β is the percentage of methanol entering the column that end up in the product draw rate and can be calculated using Equation B.1. After substituting equation B.4 we can come up with equation B.5 to also calculate β. Unlike equation B.1 this equation calculate the dynamic/”internal” state of the column and is a better reflection of the dynamics state of recovery.

βinternal =

m ˙ M eOH,Dist · 100 % m ˙ M eOH,Dist + m ˙ f usel × XM eoh,F usel

(B.5)

The most important thing is to find an accurate equation to link the fusel density and the resulting methanol fraction together. As the density is sensitive to temperature changes it is necessary to do the measurement at a specific temperature value. In this case, the defined temperature was 298.15 K. This means, the following calculations and the found equation are only valid for this particukg lar temperature. With current online density sensors an accuracy of +/ − 0.1 m 3 is achievable [Taube (2015)]. Such accurate measurements enable a very precise calculation of the recovery rate. To generate a density vs methanol fraction data the validated process simulation was used. These values were then plotted in a


165

density vs methanol fraction diagram to visualize the trend. figure B.2 shows the trend for an aqueous methanol solution at 298.15 K

Figure B.2: Methanol mass fraction against density in an aqueous solution at 298.15 K

As it can be seen the density is dropping with an increasing methanol fraction in the mixture. The highest density is therefore for pure methanol and the lowest density is for pure methanol. The connection between methanol mass fraction and density is not linear. The trend in diagram 1 can be very well described with a quadratic trend line for the interesting density section. This is also observed in literature [Green & Perry (2008)]. Equation B.6 shows the result of the regression analysis for the methanol mass fraction from the density: (please note that density values in this equation are in kilograms per litre)

XM eOH,F usel = 3.1506ρ2f usel + 10.766ρf usel + 7.2867

(B.6)

Based on equation B.6 we can now rewrite methanol mass flow rate at the fusel draw as a function of fusel volume flow rate and density. For all practical purposes we can assume the distillate flow consist of 100 % methanol and due to its commercial importance its mass flow rate (as methanol is sold on mass basis) is known. Applying this knowledge about the distillate flow rate and the fusel methanol amss flow rate estimates we can arrive at equation B.7.

166


βdensity =

m ˙ Dist · 100 % m ˙ Dist + V˙ f usel × ρ × XM eoh,F usel

(B.7)

To calculate density using equation B.7 does not require the installation of additional gas chromotrogaphs or Coriolis meters. In this instance we calculate the methanol mass flow in fusel using density and volume flow rate which require much more simpler instruments.

B.7

Sensitivity fusel stream density on recovery rate calculation

To understand if the proposed recovery measurement calculation (equation B.7) is sufficiently sensitive we carries out the following steady state sensitivity assessment. Using the recovery rate and fusel density information and assuming a simple linear relationship holds true, the sensitivity of fusel density for a 1% change in recovery rate can be calculated.

ρEtOH,M ax − ρEtOH,min RM ax − Rmin 960 − 830 sensitivity = 99.4 − 97.5 Kg sensitivity = 68.4 3 m

sensitivity =

The significance of this relatively high sensitivity value means that: • Small changes in recovery will be well reflected in the fusel density measurements • Small error in measuring fusel density will not affect the calculation of recovery based on this measurements


B.8

167

Testing

To track the robustness of the proposed method of β calculation, it was necessary to carry out step changes to variables that can effect the product recovery rate. It was determined the following variables are the most likely variables that can cause the recovery rate to fluctuate during real world operations. • Reboiler duty (can change due to fluctuations in low pressure steam that is used to supply the reboiler duty). This is the most likely process disturbance that would be encountered by the distillation column • Feed mass flow rate (can change due to upsets in upstream processes) • Methanol/water ratio (can change due to natural gas feed quality in to the plant and due to mishaps in the compression conversion process) • Ethanol content (can change due to natural gas feed quality in to the plant and due to mishaps in the compression conversion process) Since carrying out these tests on a actual plant is both costly and requires a safety review it was decided to employ the validated process model that has been already developed. Since the model can also provide actual methanol mass flow information it was decided to plot the “external” and “internal” recovery rates for comparison purposes

B.8.1

Reboiler Duty

To observe the behaviour of the recovery rate prediction to changes in reboiler duty, the reboiler duty was increased and decreased by 1 %. Figure B.3 shows the response of the three recovery rates.

168

Appendix B. Establishing a method of calculating recovery 95

95

0.98 85

External recovery Internal recovery

0.96

Density recovery

90

0.98 External recovery

0.96

Density recovery

Reboiler duty

Reboiler duty

80 0

1,000 Time in min

85

Internal recovery

Reboiler duty (MW)

90

Recovery (β)

1 Reboiler duty (MW)

Recovery (β)

1

2,000

0

500

1,000

80 1,500

Time in min

Figure B.3: Recovery response for step changes in reboiler duty

Analysis of the step up response shows that all three recovery rates βexternal , βinternal and βdensity starts at 99% and moves in a upward direction before stabilizing at new recovery rate of 99.4%. As expected βexternal increases rapidly before momentarily exceeding 100% recovery and decays down to the new recovery rate. Due to the nature of the βe xternal calculation it can exceed 100 % recovery for short periods before the mass and energy balance would force the recovery rate to decay down to the new steady state. In contrast both βinternal and Rdensity cannot exceed 100 % due to there calculations and better reflects the “state” of the column. In this instance both Rinternal and βdensity increases relatively steeply and overshoots the new recovery rate value before equilibrating. Both βinternal and βdensity have a similar shape to them although βdensity overshoots more. All three recovery rate calculations have the same start and end point. Analysis of the step down response shows that βexternal first increases before decaying down to a new steady state β of 98.5 %. The initial impulse increase of βexternal is because when the reboiler duty is stepped down the ethanol profile will momentary ”slide down” which would be detected by the product ethanol controller that in turn will increase the distillate flow rate, causing the recovery rate to increase. As in the step up case both βinternal and βdensity starts to descend rapidly and follow a similar profile to each other. however in the steady state βdensity does not come back to the same steady state value and steadies out at 98.4 %. this is a 0.1 % miss match which is on the edge of required sensitivity for a useful recovery calculation.

169


B.8.2

Feed Flow

To observe the behaviour of the recovery rate prediction to changes in feed flow rate, the feed flow was changed by 1 %. Figure B.4 shows the response of the three recovery rates.


0.96

0.98 External recovery Internal recovery

0.96

Density recovery

Density recovery

Feed flow

0

2,000

Feedflow (Kg/hr)

0.98

Recovery (β)

1 Feedflow (Kg/hr)

Recovery (β)

1

Feed flow

4,000

Time in min

0

500

1,000

1,500

2,000

Time in min

Figure B.4: Recovery response for step changes in feed flow rate

Analysis of the step up responses in figure B.4 shows that all three recovery rates moves in a downward direction before stabilizing at a lower recovery rate. As expected βexternal drops down to a low recovery rate as soon as the feed step change in completed and then slowly increase to a new steady state β of 98.5%. In comparison βinternal and βdensity move together before and slightly overshoots before stabilising. In this instance βdensity again settles down at 98.4 % which is a 0.1 % miss match between the actual value. In comparison all three recovery values reach the same steady state value of β at 99.4 % during a feed flow step down. Again βexternal shows a impulse response where it exceeds 100 % recovery momently and decays to a new equilibrium value. Both βinternal and βdensity follow a similar rise and has similar dynamic behaviour.

B.8.3

Methanol/ Water ratio

To observe the behaviour of the recovery rate prediction to changes in Methanol/water ratio, the methanol fraction of in the feed was changed by 1 %. Figure B.5 shows the response of the three recovery rates.

170



0.96

Density recovery

0.98 External recovery Internal recovery

0.96

Density recovery

Feed methanol/water fraction

0

1,000

2,000

Time in min

Figure

B.5:

methanol fraction in feed

0.98

Recovery (β)

1 methanol fraction in feed

Recovery (β)

1

Feed methanol/water fraction

0

1,000

2,000

Time in min

Recovery response for step methanol/water fraction content

changes

in

Analysis of the step up responses in figure B.5 shows that all three recovery rates moves in a downward direction before stabilizing at a lower recovery rate. The βexternal drops down then increase to a new steady state β of 98.6%, the response is a classic impulse-decay response. In comparison βinternal and βdensity move together before and slightly overshooting before stabilising to the same recovery rate. The exact opposite behaviour is observed for all three recovery rates when the methanol fraction in feed is reduced. A new steady state is reached at 99.2 % recovery.

B.8.4

Ethanol Content

To observe the behaviour of the recovery rate prediction a 25 % change was made to in the feed ethanol content. Figure B.6 shows the response of the three recovery rates.

171


100 External recovery Internal recovery

0.96

Density recovery

Recovery (β)

Recovery (β)

0.98

200

0.98 100 External recovery Internal recovery

0.96

Density recovery



0 0

1,000

PPM Ethanol in feed

1 200

PPM Ethanol in feed

1

2,000

Time in min

0 0

1,000

2,000

Time in min

Figure B.6: Recovery response for step changes in ethanol content

A general analysis of figure B.6 shows that changes in the ethanol content takes a comparatively longer dead time to effect the column recovery and takes longer to stabilize. Analysis of the step up response that all three recovery rates moves in a downward direction before stabilizing at a lower recovery rate. Unlike in other tests βexternal does not exhibit an impulse response, this is because both a change made to the feed ethanol content does not effect the methanol balance or the energy balance in the column. The effects of change in ethanol concentration is only felt by βexternal when the distillate composition controller (XC) influence the distillate draw rate to make on specification product. both βinternal and βdensity move together but are slower to react to the changes than βexternal . All three recovery rates settle down at 98.5 % recovery. A similar trend is observed for the step down response and reaches a new steady state at 99.5% recovery.

B.8.5

Practical Implications

In general both the βexternal and βinternal has the same steady state start and end points all step tests. This shows that the simplifying assumptions that negligible methanol escapes through the bottoms steam during the day to day operations envelope is valid. In additional tests illustrated that this assumption holds true for all stead state recovery rates of > 97.5%. In comparison βdensity does not always end up at the same steady state value like the other two variables. However, upon closer inspection we can see for the day to day operating envelope this simplified

172


method of predicting recovery has the ability to predict recovery rate with in +/−0.1% recovery. Considering the additional financial costs of measuring devices needed to calculate βexternal or βinternal , βdensity is the most practical solution. The dynamics of the step tests shows that βexternal to react, but tend to have a complex response in all cases. In some cases the process gain sign of βexternal changes direction mid way through the response, this can be potentially troublesome in control scheme development.

B.9

Conclusions

In this paper we developed a density based and flow rate based soft sensor to predict the product recovery of an industrial methanol distillation column. Results shows that the proposed soft sensor is able to calculate product recovery with in a +/ − 0.1% which makes it practically applicable in industry. However, it is important to note that the density based βdensity is inference based variable and is not as robust as βexternal which actually calculates the product recovery rate. As such, producers might decide to calculate the product recovery rate based on gas chromatographs even if this method has additional capital and operation expenses.

Appendix C Design of Backup control for non-routine process upsets C.1

Prolouge

This appendix explores the development of a backup control structure to operate the column during non-routine process disturbances. The objective of this control structure is to act during non-routine steam flow disturbances (main form of nonroutine upsets) and to achieve AA grade methanol specifications at relatively acceptable recovery rate. From a financial point of view, the development of a backup control scheme is critical to transition the column from current operations to high recovery operations introduced in Chapter 4. This is because high recovery operations makes the column more susceptible to non-routines disturbance and the back up control would reduce the consequences of non routine disturbances as discussed in Chapter 5

C.2

Abstract

Industrial methanol production uses extensive heat integration to achieve energy efficient operations. In an industrial methanol plant, low-pressure steam (LPS) is used to provide reboiler duty for the distillation column that is obtained from waste heat recovery. As such, the LPS can have significant fluctuations during non-routine process upsets leading to off specification methanol refining operations. In this work, a control structure base on model predictive control (MPC) is developed, as well as a PID-based control structure with a supervisory layer 173

174

Appendix C. Design of Backup control for non-routine process upsets

to control the column during these non-routine process upsets. These control schemes were tested against realistic reboiler duty disturbances that can occur in an industrial application. The tests revealed that both the MPC and PID systems control structures are able to regulate the process, even during sudden drops in reboiler duty. However, the cost of implementation and the relative simplicity will likely favour the implementation of the PID-based control structure in an industrial environment.

C.3

Introduction and Background

In methanol distillation a multi-component feed of methanol, water and ppm levels of ethanol, is refined to achieve a tight high purity product & bottoms (ppm levels) and high methanol product recovery (β) (≥ 97.5 %). For decades, operators and control schemes only focused on maintaining product ethanol specification (XD ) below the industrial AA grade limit of 10 ppm [Cheng (1994)], while β was not actively moniterd or controlled. As a safety buffer, the operators also used excess reboiler duty to create a higher than required reflux ratio which, in turn, produced product methanol with a XD of ∼ 4 ppm. This action provides the operators with a sufficient XD buffer and can negate the non-linear behaviours of the column. With plant management’s goal of improving profitability, these distillation columns now need to operate at improved product recoveries (∼ 99.5 %) using “normal levels of reboiler duty while maintaining the AA grade product methanol specification. New control structures will need to be developed to meet this objective of increasing profitability. Despite currently running the column at high reboiler duty (high reflux), methanol producers still tend to occasionally experience non routine process upsets that will put the high purity products out of specification, even with the XD buffer. If these distillation columns are operated at normal reflux, as suggested in Chapter 4, the column will be more frequently susceptible to non-routine process upsets as discussed in detail in Chapter 5. In this work, we have developed a back up control scheme based on an override logic that can manage these process disturbances. The control philosophy of this control structure is similar to the Control structures developed in Chapter 4, similarly a MPC control structure has been developed for comparison purposes. As identified in Chapter 5, reboiler duty is the main non-routine process disturbance that will create process upsets in the column. Thus, understanding how


175

a reboiler upset influences the distillation column is important in developing a control scheme that can counteract the influences. To achieve these objectives, step changes were made to reboiler duty and its effects observed. A reduction of reboiler duty by 5 • As reboiler duty is reduced all profiles (Methanol, Ethanol & Water) profiles start to “sag” as illustrated in Chapter 3. • Bottoms methanol specification has exceeded the 10ppm limit. • The ethanol concentration at the side stream has dropped by magnitudes and can no longer evacuate 95% of methanol. • As a result ethanol has built up in the column and is escaping through both bottoms and product exceeding the 10ppm product ethanol limit.

C.4

Proposed Control strategy

To run the plant at a maximum profit, methanol recovery needs to be maintained at a high level, while satisfying the constraint of AA grade methanol in the distillate. Therefore, both the proposed PID and MPC schemes are designed for the high-purity methanol distillation column. In addition, the control schemes must also be able to operate the column in a stable manner. As such, both control schemes must meet the following requirements: maximizing/controlling methanol recovery, handling the AA grade constraint on distillate ethanol content, and ability to reject disturbances in feed mass flow and composition. PID control structure. Given the above control objectives, information and the framework and control philosophy proposed in Chapter 4 we have created the control scheme illustrated in Figure figure C.1. This control scheme has two main supervisory structures the recovery constraint controller (RCC) was developed in Chapter 4. This control structure is adapted without change and controls product ethanol set-point and fusel mass flowrate based on recovery rate and product ethanol composition. This structure is supplemented with feed flowrate controller that manipulates the feed flow based on the reboiler duty. Feed flow rate is controlled by equation C.1, in which the feed flow and reboiler duty are rationed to each other. A constant (C)

176

Appendix C. Design of Backup control for non-routine process upsets is then used to adjust this ratio to achieve 99.5% during steady operations, in this case C is set at 3144. F eedf low = Reboilerduty · 1.491 + 3144

(C.1)

The mass flow rate of fusel is adjusted by equation C.2 where the mass flow rate of the fusel flow rate is adjusted between 1000 kg/hr and 5000 kg/hr to ensure all the feed ethanol is taken through the side draw. In figure C.1 this part of the controller is part of the RCC. m ˙ EtOH,F eed XEtOH,F usel

m ˙ F usel =

(C.2)

Condenser PC

RCC PT

XT FC XT

FT

XT

FT

Distillate FC XT

FT

Feed FC BTU

Refining Column

Side Draw

LT

Reboiler

LC

Bottoms

Figure C.1: A simplified control diagram of the proposed control structure

C.5

MPC

Model predictive control (MPC). The methanol distillation column can be controlled by a 2ÃŮ2 multi-input multi-output (MIMO) controller, with each


177

input affecting both outputs. The two inputs are the methanol recovery, Îš, and the ethanol content in the distillate stream. The two outputs of the MPC are the set point for the XD controller and the reboiler duty. A schematic of the MPC scheme is shown in Figure C.2. As previously described, the two inputs are the ethanol content in the distillate, XD, and the methanol recovery.

Condenser MPC PC

LT

LC

PT

FC XT FT

FT

Distillate FC

Feed

FT

Refining Column

Side Draw

BTU LT

Reboiler

LC

Bottoms

Figure C.2: A simplified MPC diagram

C.6

Disturbance testing

To tests the ability of both the proposed control structure and the MPC to handle “realistic” process disturbances, they were subjected to sine shape drops in reboiler duty. The period of this sine wave, shaped drops were varied from 50 min to 2000 min. The sine wave drops started at 90MW and were reduced to 60MW, before returning to the starting value, this type of

178

Appendix C. Design of Backup control for non-routine process upsets a sine shaped drop was selected over a classic step change as this simulated a more realistic reboiler duty drop. Firstly, the reaction of the two control schemes to a 50 min drop in reboiler duty. Analysis of Figure 2a reveals that both the control structures are able to maintain XD specification below 10 ppm. In Figure C.3a, the proposed control scheme has a variation of between 99.6% and 100%, in comparison the MPC system has a fluctuation where drops to as low as 98.0% and reach a maximum value of about 100%. As such, in this instance the proposed supervisory control structure preforms a better job. Analysis of Figure Figure C.3d, illustrate that both the schemes are able to maintain the bottoms methanol specification. The maximum methanol concentration of the MPC controlled column reached 0.1 ppm, in comparison the proposed controller reached a concentration of 3 ppm. As such, the MPC controller is able to control the methanol composition more tightly in comparison to the proposed control structure.

179

Appendix C. Design of Backup control for non-routine process upsets ·10−5

QReb

0.80 0.6

0.60 0

β Prop.

1.20 1.00

0.8

QReb

0.8

1.10

0.6

1.00

500 1,000 1,500 2,000

0

500 1,000 1,500 2,000

Time in s

Time in s

(a) Change in XD

(b) Change in β

·105

·105

1.0

1.20

β MPC

QReb in kW

QReb in kW

XD MPC

XB in -

XD Prop.

·105

1.0

β in -

·105

1.0

·10−6

·105

1.0

3.00

QReb

0.6

Feed Prop

2.00

0.8

QReb

0.6

XB MPC

Feed MPC

0

1.00 500 1,000 1,500 2,000 Time in s (c) Feed flow rate change

1.00

XB Prop.

0

0.00 500 1,000 1,500 2,000 Time in s (d) Change in XB

Figure C.3: Short term reboiler duty disturbance testing with a 50 minute sine wave.

We then analysed the reaction of the two control schemes to a 2000 min drop in reboiler duty. Analysis of Figure C.5a illustrates that both the control schemes are able to maintain XD specification even during large changes in duty change. Observations made about shows both control structures maintain beta at about 99.6%. Similar observations can be made about the bottoms methanol that is always below 0.1 ppm. Table 1 the reaction of the two control structures during these reboiler duty disturbances is quantified. During the 50 minute disturbance tests in the reboiler duty the average feed accommodated by the Proposed control structure is 1.34e+05 kg/h, while the MPC accommodates 1.30e+05 kg/h. This means on average the proposed control structure is able to refine more methanol even in comparison to a

XB in -

1.20

QReb in kW

0.8

Feed in kg/h

QReb in kW

1.40

180

Appendix C. Design of Backup control for non-routine process upsets MPC during short time period reboiler disturbances. During 2000-minute disturbance tests both control structures refine 1.31e+05 kg/h of feed The average of Recovery rate for tshort reaches the highest value of 99.96%, at the system without MPC.

1.20

1.20

XD MPC

QReb in kW

·105

1.0

1.00

0.8

0.80 0.6

1.10

0.8

β in -

XD Prop

·10−5

QReb in kW

QReb

XD in -

·105

1.0

QReb β Prop. β MPC

1.00

0.6

0.60

0.90 1

2

3

4

0

·104

Time in s

3

4 ·104

(b) Sine wave 2000 min of β at change of QReb .

·105

·105

2 Time in s

(a) Sine wave 2000 min of XD at change of QReb .

1.0

1

·10−6

·105

1.0

QReb

1.20

Feed Prop Feed MPC

0.6

QReb in kW

0.8

4.00 Feed in kg/h

QReb in kW

1.40 0.8 QReb

0.6

2.00

XB Prop. XB MPC

1.00 0

1

2 Time in s

3

4 ·104

(c) Sine wave 2000 min of Feed at change of QReb .

0.00 0

1

2 Time in s

3

4 ·104

(d) Sine wave 2000 min of XB at change of QReb .

Figure C.4: Disturbance testing 2000min.

XB in -

0

181

Appendix C. Design of Backup control for non-routine process upsets Table C.1: Maximum values of step test.

Variable

System

Step test

Sine wave tshort

Sine wave tlong

XD max (ppm)

Without MPC MPC new

10.6 12.4

9.75 10.2

8.45 7.26

XB max (ppm)

Without MPC MPC new

0.12 3.38

3.08 0.12

5.0e-04 1.17e-03

Feedflow (kg/h)

Without MPC MPC new

– –

133967.35 130469.28

131330.23 131017.35

β (%)

Without MPC MPC new

– –

99.96 99.60

99.61 99.64

C.7

Conclusion

For Reboiler duty disturbances in form of a decending sine waves each of the two systems can be used to produce methanol and wastewater that are within the specifications. The most economical systems are the one that runs the distillation column with high Feed and high Recovery rate. The most economical system is the one without MPC. The new MPC system is promising but more adjustments have to be done in terms of tuning to reach similar values to the system without MPC. If there is a sudden drop of Reboiler duty from 90MW to 60MW, both systems stay within the limit for XB. Again, the system without MPC produces the best values. As both of them do not keep the upper limit of XD, more adjustments, even to the system without MPC, have to be done. Future research can be directed to retrofit the design of the column to exhibit inherent ability to reject disturbance by integration of process and controller design concepts.

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