Control Loop Foundation for Batch and Continuous Control

Control Loop Foundation for Batch and Continuous Control GREGORY K MCMILLAN

use pure black and white option for printing copies

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Presenter

– Greg is a retired Senior Fellow from Solutia Inc. During his 33 year career with Monsanto Company and its spin off Solutia Inc, he specialized in modeling and control. Greg received the ISA “Kermit Fischer Environmental” Award for pH control in 1991, the Control Magazine “Engineer of the Year” Award for the Process Industry in 1994, was inducted into the Control “Process Automation Hall of Fame” in 2001, and honored by InTech Magazine in 2003 as one of the most influential innovators in automation. Greg has written a book a year for the last 20 years whether he needed to or not. About half are humorous (the ones with cartoons and top ten lists). Presently Greg contracts via CDI Process and Industrial as a principal consultant in DeltaV Applied R&D at Emerson Process Management in Austin Texas. For more info visit: – http://ModelingandControl.com – http://www.easydeltav.com/controlinsights/index.asp (free E-books) 11/10/2008

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See Chapter 2 for more info on “Setting the Foundation”

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See Chapters 1-7 for the practical considerations of improving tuning and valve dynamics

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See Appendix C for background of the unification of tuning methods and loop performance

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See Chapter 1 for the essential aspects of system design for pH applications

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Overview )

This presentation covers highlights or low lights of current loop performance and how to improve batch and continuous processes: – – – – – – – – – – – – – –

Pyramid of Technologies Valve and Flow Meter Performance Process Control Improvement Examples Basic Control Opportunities Summary Reactors and Column Loop Tuning Facts of Life Transfer of Variability for Batch Sources of Disturbances Transition from Basic to Advanced Regulatory Control of Batch Online Data Analytics for Batch and Continuous Processes Virtual Plant Uses and Fidelities of Dynamic Process Models What we Need Columns and Articles in Control Magazine

Pyramid of Technologies TS APC is in any technology that integrates process knowledge

RTO LP/QP

The greatest success has been Achieved when the technology closed the loop (automatically corrected the process without operator intervention)

Ramper or Pusher Model Predictive Control

Foundation must be large and solid enough to support upper levels. Effort and performance of upper technologies is highly dependent on the integrity and scope of the foundation (type and sensitivity of measurements and valves and tuning of loops)

Property Estimators Fuzzy Logic Abnormal Situation Management System Process Performance Monitoring System Loop Performance Monitoring System Auto Tuning (On-Demand and On-line Adaptive Loop Tuning) Basic Process Control System TS is tactical scheduler, RTO is real time optimizer, LP is linear program, QP is quadratic program

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Loops Behaving Badly A poorly tuned loop will behave as badly as a loop with lousy dynamics (e.g. excessive dead time)!

1 Ei = ------------ ∗ Ti ∗ Eo Ko ∗ Kc

You may not want to minimize the integrated error if the controller output upsets other loops. For surge tank and column distillate receiver level loops you want to minimize and maximize the transfer of variability from level to the manipulated flow, respectively.

where: Ei = integrated error (% seconds) Eo = open loop error from a load disturbance (%) Kc = controller gain Ko = open loop gain (also known as process gain) (%/%) Ti = controller reset time (seconds) (open loop means controller is in manual) Tune the loops before, during, and after any process control improvements 11/10/2008

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Unification of Controller Tuning Settings All of the major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve, Simplified Internal Model Control, and Lambda) reduce to the following form for the maximum useable controller gain

τ1 K c = 0.5 * Ko ∗θ max Where: Kc = controller gain Ko = open loop gain (also known as process gain) (%/%) τ1 = self-regulating process time constant (sec) θmax = maximum total loop dead time (sec)

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Definition of Deadband and Stick-Slip Dead band is 5% - 50% without a positioner ! Deadband

Pneumatic positioner requires a negative signal to close valve

Stroke (%)

Stick-Slip 0 Deadband

Signal (%) Digital positioner will force valve shut at 0% signal

The effect of slip is worse than stick, stick is worse than dead band, and dead band is worse than stroking time (except for surge control) Stick-slip causes a limit cycle for self-regulating processes. Deadband causes a limit cycle in level loops and cascade loops with integral (reset) action. If the cycle is small enough it can get lost in the disturbances, screened out by exception reporting, or attenuated by volumes 11/10/2008

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Saw Tooth Flow Controller Output Limit Cycle from Stick-Slip

Controlled Flow (kpph) Square Wave Oscillation

Controller Output (%) Saw Tooth Oscillation

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Rounded Level Controller Output Limit Cycle from Deadband

Controlled Level (%) Saw Tooth Oscillation

Controller Output (%) Rounded Oscillation

Manipulated Flow (kpph) Clipped Oscillation

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Identification of Stick and Slip in a Closed Loop Response 59 58.5 58

Controller Output

Flow

57.5

3.25 Percent Backlash + Stiction

57 56.5 Stroke %

56

stick

55.5 55 54.5

Dead band is peak to peak amplitude for signal reversal

slip

54 53.5 53 0

100

200

300

400 500 Time ( Seconds )

600

700

800

The limit cycle may not be discernable due to frequent disturbances and noise

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Response Time of Various Positioners (small actuators so slewing rate is not limiting)

Response time increase dramatically for steps less than 1%

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Control Valve Facts of Life ) ) ) )

) ) ) )

)

Pneumatic positioners are almost always out of calibration Most tests by valve manufacturers for stick-slip are at 50% with loosely tightened stem packing to minimize seating, sealing, and packing friction Without a representative position feedback in the control room, it is anybody’s guess what the valve is doing unless there is a low noise sensitive flow sensor Not all positioners are equal. Pneumatic positioners, especially the spool or single amplification stage low gain ones will increase the valve response time by an order of magnitude (4 -> 40 sec) for small changes in controller output All valves look good when checking positions for 0, 25, 75, and 100% signals Valve specs do not generally require that the control valve actually move The tighter the shutoff, the greater the stick-slip for positions less than 20% Smart positioner diagnostics and position read back are lies for actuator shaft position feedback of rotary type isolation valves posing as throttling valves particularly for pinned rather than splined shaft connections due to twisting of the shaft. Field tests show stick-slip of 85 in actual ball or disc movement despite diagnostics and read back indicating a valve resolution of 0.5% The official definition of valve rangeability is bogus because it doesn’t take into account stick-slip near the seat. Equal percentage valves with minimal stickslip (excellent resolution and sensitivity) generally offer the best rangeability 11/10/2008

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Top Ten Signs of a Valve Problem (10) The pipe fitters are complaining about trying to fit a 1 inch valve into a 10 inch pipe. (9) You bought the valve suppliers’ “monthly special.” (8) A butterfly disc won’t open because the ID of the lined pipe is smaller than the OD of the disc. (7) The maintenance department personally put the valve on your desk. (6) A red slide ruler was used to size a green valve. (5) Your latest valve catalog is dated 1976. (4) The maintenance department said they don’t want a double seat “A” body. (3) The valve was specified to have 0% leakage for all conditions including all signals. (2) The fluid field in the sizing program was left as water. (1) The valve is bigger than the pipe.

Flow Meter Performance

) ) ) ) )

Type Coriolis Magmeter Vortex Orifice

Sizes ¼ -8” ¼-78” ½-12” ¼-78”

Range 100:1 25:1 9:1* 4:1

Piping 1/1 5/1 10/5 10/5

Interferences solids, alignment, vibration conductivity, electrical noise profile, viscosity, hydraulics profile, Reynolds Number

Reproducibility 0.1% of rate 0.5% of rate 1.0% of span 5.0% of span

* - assumes a minimum and maximum velocity of about 1 and 9 fps, respectively

Coriolis flow meters via their accurate density measurement offer direct concentration measurements for 2 component mixtures and inferential measurements for complex mixtures.

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Neutralizer Control – “Before”

Reagent Stage 2 Reagent Stage 1 FC 1-2

FT 1-2

FT 2-1

AC 1-1

FT 1-1

AC 2-1

AT 2-1

AT 1-1 Static Mixer

Feed

2 pipe diameters Neutralizer

Discharge

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Nonlinearity and Sensitivity of pH pH

8 6

Good valve resolution or fluid mixing does not look that much better than poor resolution or mixing due amplification of X axis (concentration) fluctuations

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Reagent Flow Influent Flow or Reagent Charge Process Volume

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Neutralizer Control – “After” Feedforward Summer

RSP

Σ FT 1-1

Signal Characterizer AC 1-1

FC 1-2

FC 2-1

FT 2-1

Reagent Stage 2

f(x)

Reagent Stage 1

*1 *1

FT 1-2

AT 1-1

Static Mixer Feed 20 pipe diameters

*1 - Isolation valve closes when control valve closes

AC 2-1 Neutralizer AT 2-1

Discharge

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Distillation Column Control – “Before” PC 3-1 LT 3-1

LC 3-1

Vent

Feed Tank Distillate Receiver PT 3-1 Overheads

Reflux FC 3-3

Thermocouple Tray 10

FT 3-3

TE 3-2

TC 3-2

Column Feed

FC 3-4

LC 3-2

FT 3-4

LT 3-2

Storage Tank

Steam Bottoms

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Nonlinearity and Sensitivity of Tray Temperature Operating Point Temperature

Measurement Error

Tray 6

Measurement Error

Tray 10

Distillate Flow Feed Flow % Impurity Impurity Errors

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Distillation Column Control – “After” PC 3-1

Feedforward summer LC 3-1

Σ

FT3-3 Feed Tank

LT 3-1

Vent

RSP FC 3-1 PT 3-1

Distillate Receiver

RSP

FT 3-1

FT 3-2

Overheads Feedforward summer

Reflux FC 3-3

FT 3-3

RTD Feed

Column Tray 6

TT 3-2

FT3-3 Signal Characterizer

LT 3-2

FT 3-4 Steam

Σ

TC 3-2

f(x)

Storage Tank

LC 3-2

FC 3-4

FC 3-2

RSP FC 3-5

FT 3-5 Bottoms

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When Process Knowledge is Missing in Action PV distribution for original control

2-Sigma

LOCAL Set Point

Upper Limit

2-Sigma

RCAS Set Point

Extra margin when “war stories” or mythology rules

value PV distribution for improved control

Good engineers can draw straight lines Great engineers can move straight lines 2-Sigma

2-Sigma

Benefits are not realized until the set point is moved! (may get benefits by better set point based on process knowledge even if variability has not been reduced) 11/10/2008

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Top Ten Ways to Impress Your Management with the Trends of a Control System (10) Make large set point changes that will zip past valve dead band and local nonlinearities (9) Change the set point to operate on the flat part of the titration curve (8) Select the tray with minimum process sensitivity for column temperature control (7) Pick periods when the unit was down (6) Decrease the time span so that just a couple data points are trended (5) Increase the reporting interval so that just a couple data points are trended (4) Use really thick line sizes (3) Add huge signal filters (2) Increase the process variable scale span so it is at least ten times the control region of interest (1) Increase the historian’s data compression so that most changes are screened out as insignificant

Basic Opportunities in Process Control )

Decrease stick-slip and improve the sensitivity of the final element (Standard Deviation is the product of stick-slip, valve gain, and process gain) ‰ ‰ ‰ ‰

)

Use properly tuned smart positioners, short shafts with tight connections, and low friction packing and seating surfaces to decrease valve slip-stick and dead band (do not use isolation valves for throttling valves) If high friction packing must be used, aggressively tune the smart positioner Improve valve type and sizing and add signal characterization to increase valve sensitivity Use variable speed drives where appropriate for the best sensitivity

Improve the short and long term reproducibility and reduce the interference and noise in the measurement (Standard Deviation is proportional to reproducibility and noise) ‰ ‰ ‰

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Use magnetic and Coriolis mass flow meters to eliminate sensing lines, improve rangeability, and reduce effect of Reynolds Number and piping Use smart transmitters to reduce process and ambient effects Use RTDs and digital transmitters to decrease temperature noise and drift

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Basic Opportunities in Process Control )

Reduce loop dead time (Minimum Integrated Error is proportional to the dead time squared) ‰ ‰ ‰ ‰ ‰

) ) ) ) ) )

Decrease valve dead time (stick and dead band) Decrease transport (plug flow volume) and mixing delay (turnover time) Decrease measurement lags (sensor lag, dampening, and filter time) Decrease discrete device delays (scan or update time) Decrease analyzer sample transport and cycle time

Tune the controllers (Integrated Error is inversely proportional to the controller gain and directly proportional to the controller integral time) Add cascade control (Standard Deviation is proportional to the ratio of the period of the secondary to the process time constant of the primary loop) Add feed forward control (Standard Deviation is proportional to the root mean square of the measurement, feed forward gain, and timing errors) Eliminate or slow down disturbances (track down source and speed) Add inline analyzers (probes) and at-line analyzers with automated sampling since ultimately what you want to control is a composition Optimize set points (based on process knowledge and variability) ‰

To realize the benefit of reduced variability, often need to change a set point

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Reset Gives Them What They Want TC-101 Reactor Temperature Out

PV

SP

steam valve opens

temperature

Reset won’t open the water valve Until the error changes sign, PV goes above the set point. Reset has no sense of direction. set point (SP)

50%

PV water valve opens

Should the steam or water valve be open?

?

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Reset action integrates the numeric difference between the PV and SP seen by operator on a loop faceplate Reset works to open the steam valve

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time

Proportional and rate action see the trajectory visible in a trend! Both would work to open the water valve to prevent overshoot.

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Reactor and Column Loop Tuning

)

Most reactor and column composition, gas pressure, and temperature loops have too much integral action (reset time too small), not enough proportional action (gain too small), and not enough derivative action (rate time too small). ‰ Rate time should be 0.1x process time constant or 0.1x reset time with a minimum value of sensor lag time. ‰ Rate action is essential for exothermic reactors that can runaway

)

Often these loops are “near integrators” due to a large process time constant . Batch processes often have “true integrators” because of a lack of self-regulation (no steady state). Whether “near integrators” or “true integrators”, these loops require much more gain action to impose self-regulation and provide pre-emptive action. There is a window of allowable gains where too low of a controller gain will result in slow rolling oscillations from reset. ‰ (controller gain) * (controller reset time) > 4 / (integrating process gain)

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Modeling and Control Facts of Life

)

“Timing is Everything” ‰ In life, business, and process control (especially feedforward)

)

“Without Dead Time I would be Out of Job” ‰ If the dead time was zero, the only limit to how high you can set the controller gain or how tight you can control is measurement noise ‰ Unlike aerospace, the process industry has large and variable time delays and time lags from batch cycle times, vessel mixing times, volume residence times, transportation delays, resolution limits, dead band, and measurements ‰ Total dead time is sum of time delays and all time lags smaller than largest ‰ Best possible integrated absolute error is proportional to dead time squared

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Modeling and Control Facts of Life )

Models (experimental or theoretical) allow you to take the blindfold off ‰ Models convey process knowledge and provide insight on what has changed and what should be improved (e.g. largest source of dead time) ‰ “War stories rule” where there are no models ‰ “Mythology rules” where there are no models ‰ “Benefits are hearsay” where there are no models

)

Nonlinearity is a reason to build models rather than avoid models ‰ Unless you want job security for constantly retuning controllers. Also, implied in most techniques is some model (e.g. reaction curve method) ‰ Tight control greatly reduces the operating point nonlinearity (e.g. pH) and secondary flow loops eliminate the valve nonlinearity for higher level loops ‰ Signal characterization on the controller output (based on a model of the installed valve characteristic) greatly reduces the valve nonlinearity

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Speed of Various Sources of Disturbances (Speed Kills) )

Process ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

)

A loop can catch up to a slow disturbance. Liquid pressure Is the fastest upset (travels at the speed of sound in liquid).

Equipment ‰ ‰

)

Flow (fast) Gas pressure (fast) Liquid Pressure (very fast) Raw Materials (slow) Recycle (very slow) Temperature (slow) Catalyst (slow) Steam (fast) Coolant (fast) Fouling (slow) Failures (fast)

Environmental ‰ ‰ ‰

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Day to Night (slow) Rain Storms and fronts (fast) Season to Season (very slow) 33

Speed of Various Sources of Disturbances (Speed Kills) )

Valves ‰ ‰ ‰

)

Measurements ‰ ‰ ‰

)

Stick-slip (fast) Split Range (fast) Failures (very fast) Noise (very fast) Reproducibility (fast) Failures (very fast)

Controllers ‰ ‰ ‰ ‰

Feedback Tuning (fast) * Feed forward Timing (fast) Interaction (fast) Failures (very fast)

* Most frequent culprit is an oscillating level loop primarily due to excessive reset action

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Speed of Various Sources of Disturbances (Speed Kills) )

Market* ‰ ‰

)

Operators ‰ ‰ ‰

)

Rate changes (fast) Product transitions (fast) Manual operation (fast) Sweet spots (fast) Inventory control (fast)

Discrete ‰ ‰ ‰ ‰ ‰

On-off control (very fast) Sequences (fast) Batch operations (fast) Startup and shutdown (very fast) Interlocks (very fast)

*For minimized inventory, changes in market demand can result in fast production rate changes and product grade or type transitions

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Batch Control Variability Transfer from Feeds to pH, and Reactant and Product Concentrations

Feeds

Concentrations Optimum Product

Reactant Product Reagent

Optimum pH pH Reactant Optimum Reactant

Most published cases of multivariate statistical process control (MSPC) use the process variables and this case of variations in process variables induced by sequenced flows. 11/10/2008

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PID Control Variability Transfer from pH and Reactant Concentration to Feeds

Concentrations

Feeds

Optimum Product Reactant Product

Reactant pH Reagent Optimum pH

Optimum Reactant

The story is now in the controller outputs (manipulated flows) yet MSPC still focuses on the process variables for analysis 11/10/2008

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Model Predictive Control Variability Transfer from Product Concentration to pH, reactant Concentration, and Feeds

Feeds

Concentrations Reactant Optimum Product Product

pH

Reagent

Optimum pH

Reactant Optimum Reactant

Time

Time

Model Predictive Control of product concentration batch profile uses slope for CV which makes the integrating response self-regulating and enables negative besides positive corrections in CV 11/10/2008

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Example of Basic PID Control TC-3

CTW

vent PC-1 condenser FC-1

feed A RC-1 ratio control

CAS

feed B

TT FT PT FC-2 TT

FT

TC-1 cascade control CAS

coolant makeup

TC-2

TT

Conventional Control

reactor product

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Example of Advanced Regulatory Control override control maximum production rate

ZC-3


10 biological) ‰ Product development, process design, real time optimization, advanced control prototyping and justification, process control improvement, diagnostics, training

)

Smart wireless integrated process and operations graphics ‰ Online process, loop, and advanced control metrics for plants, trains, and shifts ‹ ‹ ‹

Yield, on-stream time, production rate, utility cost, raw material cost, maintenance cost* Variability, average % of max speed (Lambda), % time process variable or output is at limits, % time in highest mode, % deadband, % resolution, number of oscillations Process control improvement (PCI) benefits ($ of revenue and costs)

‰ 3-D, XY, future trajectories of process and performance metrics response, data analytics, worm plots, and trends of automatically selected correlated variables )

Coriolis flow meters, RTDs, and online and at-line analyzers everywhere ‰ Real time analysis via probes or automated low maintenance sample systems ‰ Automated time stamped entry of lab results into data historian ‰ Online material, energy, and component balances

)

Control valves with < 0.25% resolution and < 0.5% dead band

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Key Points

) ) ) ) ) ) ) ) ) ) ) )

Tune the loops Use digital positioners and throttle valves to get resolution better than 0.5% Use Coriolis and Magmeters to get accuracy better than 0.5% of rate Tune the loops Add cascade and feed forward control for disturbances Model the process to dispel myths and build on process knowledge Improve the set points Add composition control Reduce the size and speed of disturbances Transfer variability from most important process outputs Add online data analytics (multivariate statistical process control) Add online metrics to spur competition, and to adjust, verify, and justify controls

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Control Magazine Columns and Articles

) ) ) ) ) ) ) ) )

“Control Talk” column 2002-2008 “Has Your Control Valve Responded Lately?” 2003 “Advanced Control Smorgasbord” 2004 “Fed-Batch Reactor Temperature Control” 2005 “A Fine Time to Break Away from Old Valve Problems” 2005 “Virtual Plant Reality” 2005 “Full Throttle Batch and Startup Responses” 2006 “Virtual Control of Real pH” 2007 “Unlocking the Secret Profiles of Batch Reactors” 2008

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