Slug Detection as a Tool for Predictive Control of GLCC© Compact Separators
Shankar Earni Shoubo Wang
Current design and performance of the GLCC© separator is dependent on the prediction of the upstream inlet flow conditions based on available models. It is expected that early detection of terrain slugging (slug length, slug velocity and holdup) and controlling the liquid level in the GLCC using feed forward mechanism can improve the operational range of the GLCC, by decreasing the gas carry under and liquid carry over, and thereby decreasing the control valve dynamics. The conventional feedback control loops can seldom achieve perfect control considering the impact of huge slugs that are keeping the output of the process continuously away from desired set point value. The reason is simple: a feedback controller reacts only after it has detected a deviation in the value of the level from the set point. Unlike the feedback systems, a feed forward control configuration measures the disturbance directly and takes control action to negate the effect of the disturbance on liquid level in the GLCC. Therefore, a feed forward control system has the theoretical potential for perfect control if the slug detection and characterization are perfect. A strategy for GLCC predictive control has been proposed which integrates the feedback and feed forward loops to compensate for error due to modeling and slug characterization. A model has been developed for predictive control system design and simulated in MATLAB-Simulink®. Experimental results obtained demonstrate that the proposed strategy is a viable approach for GLCC predictive control. 关DOI: 10.1115/1.1521711兴
Ram S. Mohan Ovadia Shoham Mechanical and Petroleum Engineering Departments, The University of Tulsa, Tulsa, OK 74104
Jack D. Marrelli ChevronTexaco E&P Technology Division, Humble, TX 77338
Introduction 1
Compact separators such as GLCC have proven to provide considerable impact in improving the optimization and productivity of the petroleum industry 共Marrelli et al. 关1兴兲. There is an increasing need to develop appropriate control strategies, design tools and simulators for the GLCC to improve the performance under slug flow conditions, as its residence time is very small. Slug flow can be mitigated by slug catchers or by some flow conditioning techniques. Another way to handle such fluctuations in gas and liquid flow rates and to avoid unplanned shutdowns of the production system is by having appropriate control strategy of downstream process equipment. Performance of the conventional feedback control system by manipulating the upstream control valve, in order to maintain the level in the GLCC, is not completely sufficient during the onset of severe slugs. A feedback controller reacts only after it has detected a deviation in the value of the output from the desired set point. So it makes perfect sense if one can estimate the disturbance that is coming into the system and provide suitable control action in order to negate the effect of the disturbance. This is the main principle of the feed forward control system. Several studies were conducted to characterize the slug in twophase flow. Andreussi et al. 关2兴 characterized the slug using a pair of conductance probes made of two ring electrodes mounted flushed to the pipe wall. Dhulesia et al. 关3兴 developed a method to detect slugs in multiphase flow line based on the analysis of vibrations 共acoustic principles兲 of pipeline structures caused by the flow of fluid. This information in the control room allowed the operator to take appropriate control actions on the downstream facilities depending upon the slug length. Barnea et al. 关4兴 successfully used conductance probes to identify two-phase flow pat1 GLCC©-Gas-Liquid Cylindrical Cyclone-copyright, The University of Tulsa, 1994. Contributed by the Petroleum Division for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received by the Petroleum Division Mar. 2001; revised manuscript received Jul. 2002. Associate Editor: J. K. Keska.
terns in horizontal, near horizontal and upward flows in air-water systems. This study provided the knowledge required to construct and implement the conductance probes used to measure slug dissipation for the current investigation. Wang 关5兴 and Mohan et al. 关6兴 have developed a steady-state model and dynamic model for GLCC control and performed a sensitivity analysis and control system design. As a continuation of this work, Wang et al. 关7,8兴 conducted detailed experimental investigations to evaluate the improvement in the GLCC operational envelope for liquid carryover with the integrated level and pressure control system, for a wide range of flow conditions. Their detailed experimental studies demonstrated that by using feedback control systems, the GLCC operational envelope for liquid carry-over could be increased by three folds in the high liquid flow and gas flow regions. The optimal control strategy of Wang 关9兴 has the advantages of handling large flow variations with minimum pressure drop across the GLCC. Payne et al. 关10兴 have developed a feed forward slug flow mitigation control system. This technique involves liquid slug flow control by manipulating the control valve, which is placed in the flow-line upstream of gas-liquid separator 共conventional兲, based upon the signal from the slug detector, a device for measuring the presence and the volume of the slug in the flow line. Objective of the current work is to develop a feed forward control system to improve the performance of a GLCC under severe slugging conditions and integrate it with the feedback control system. Towards this objective a mathematical model has been developed to conduct the control system design. Subsequently dynamic simulations are performed to evaluate the design. The developed control strategies are implemented in a real GLCC system and detailed experimental investigations are conducted to evaluate the effectiveness of the control strategies. In this study, the features of the GLCC dynamic model, the control system design and the dynamic simulators are developed using MATLAB/Simulink® software for evaluation of several different GLCC control philosophies for two-phase flow metering loop and bulk separation applications. A predictive control strategy, integrating feed forward and feedback control schemes is proposed
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for real time control of GLCC. This strategy has the capability of maintaining the liquid level close to its set point, in spite of large flow fluctuations. This feed forward control augments the existing control system to realize the control objectives more effectively, thereby increasing the operational efficiency of the GLCC, and providing the petroleum industry with another effective tool for slug mitigation.
Predictive Liquid Level Control System Figure 1 shows the schematic of a GLCC equipped with control systems. The control systems can be broadly classified into feedback and feed forward control systems. The feedback control loops could be liquid level control by LCV and pressure control by GCV. In the LCV liquid level control loop, the liquid level sensor 共such as a differential pressure transducer兲 measures the liquid level and sends the signal to the liquid loop controller. The controller 共LC1兲 sends the actuating signal to drive the liquid control valve so as to manipulate the liquid outflow and control the liquid level. The GLCC feedback control system is discussed in more detail by Wang 关9兴. The main concept of GLCC feed forward control is liquid flow rate control. In the liquid flow feed forward control loop, a liquid slug is characterized and measured by suitable sensors upstream of the GLCC and the signal is sent to the feed forward liquid control loop controller. The output signal from the controller drives the liquid control valve to match the liquid outflow rate to
Fig. 1 Schematic of GLCC control systems
the inflow rate 共disturbance兲, so that the liquid level in the GLCC can be maintained around the set point. Any time lag between the inflow and outflow will affect the level in the GLCC. The level will then either rise or fall, depending on the direction of the flow rate change, causing the level controller to adjust the outflow proportionally. This control system always returns level to the set point, with a control valve offset proportional to the error between the actual flow and measured flow from slug characterization. The only serious drawback of the feed forward technique is its dependency on plant accuracy 共accuracy of modeling the full system兲 and accuracy of disturbance characterization. To provide perfect feed forward control, a system must model the plant exactly; otherwise whatever error may exist in positioning the manipulated variable causes offset. Errors may arise from several sources, namely, inaccuracies in the measurements of load and manipulated variables, errors in calculations, failure of the model to represent the characterization of the plant adequately and exclusion of significant load components from the feed forward system. Ongoing improvements in slug detection and use of sophisticated data acquisition tools have been helpful in reducing these errors. The feed forward control system cannot be all-inclusive. Some load components are so slight, or invariant or ill defined that their inclusion is not warranted. All these errors contribute to considerable offset. If this offset is large, this can upset the stability of the system. To counter this offset, some means must be provided for recalibration while the system is operating. In general, this can be done by adding a feedback control loop in order to take care of the small deviations from the set point, which result from the errors. Feed forward and feedback controllers can be combined in several different ways. One possible configuration for feed forwardfeedback control is to add the outputs of the feed forward and feedback controllers together and send the resulting signal to the final control element. This is the configuration implemented in this study as shown in Fig. 2. The primary advantage of this configuration is that the feed forward controller does not affect the stability of the feedback control loop. In general, since feed forward control is warranted only on the most demanding and most difficult situations, integral is the only useful feedback mode. The feedback controller should have the same control modes as it would without feed forward control, but the settings should not be as restrictive. The feed forward control system then positions the manipulated variable so that the error in the controlled variable disappears. If acted upon by closely set feedback, the correct position will be altered, producing another disturbance that prolongs the settling time of the system. To this extent, the settings of the feedback controller, regardless of what modes have been selected, should be relaxed.
Fig. 2 Integrated feed forward and feedback level control loop
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An adaptive control system can be defined as having the ability to change its parameters in accordance with the changing flow conditions. A feed forward control system, by itself, can only generate an output relative to known and measurable inflow conditions, as prescribed by the given control algorithm. Some factors relating to the process may be unknown and variable, for example, the slug characteristics, may change while flowing from the slug detector to the GLCC. For optimum performance, the feed forward control system should be supplied with information regarding these unknowns. A feedback controller, on the other hand, is geared to solve for unknown variable, as it knows the resultant of the unknown. So the inclusion of feedback control system in a feed forward loop actually adapts the feed forward loop to unmeasured changes in the process. This mutual adaptation is a further indication of how perfectly feed forward and feedback control complement each other if designed properly. Feed forward control is fast, intelligent, and responsive but also inaccurate; feedback control is slow but accurate and is capable of regulation for unknown load conditions.
Controller Design and Dynamic Simulation The linear transfer functions of the system are a stepwise mathematical description of the sub-system behavior and are always stated in Laplace domain. The deviation variable used below is defined as the deviation of a variable from its steady state or set point value. The derivation of the transfer function is in the order of block numbers given in Fig. 2. The blocks that constitute the feedback control loop are: Block1: This is the transfer function of the liquid level controller. Block2: This is the transfer function describing the pneumatic line and actuator delay. Block3: This is the transfer function for the LCV dynamics and characteristics. Block4: The Laplace transformation of the relationship between the liquid level and the GLCC liquid volume. Block5: This is the transfer function representing the liquid level sensor/transmitter. The feedback model is discussed in more detail by Wang 关9兴. Brief procedure of the feedback controller design is given below. Feedback Controller Design. There are several design tools for feedback control system design and analysis. Most common techniques are the root locus and the frequency domain methods. Detailed description of both techniques can be found in Earni 关11兴. The following is the frequency domain approach that is followed to design the feedback controller. PID means Proportional plus Integral plus Derivative. The integral term is most effective at
low frequencies, the proportional term at moderate frequencies and the derivative term at higher frequencies. These frequencies are relative to the bandwidth of the process. To achieve our design specifications: 1. Start by choosing both the zeros of the PID-Controller one decade below the crossover frequency required. 2. A program has been written to calculate the magnitude and phase of frequency response at different frequencies. The magnitude plots for closed loop transfer function for different cases are given in Fig. 3. 3. From Fig. 3 one can find that with increase in the distance of the zeros of the PID-Compensator from the imaginary axis, the magnitude peak increases indicating that there is a decrease in the damping ratio. But a decrease in the damping ratio increases the percentage overshoot or number of overshoots and increases the settling time. On the other hand, with a decrease in the distance of the zeros of the PIDCompensator, the peak decreases indicating that there is an increase in the damping ratio. An increase in the damping ratio decreases the percentage overshoot and decreases the settling time. So, the controller settings can be optimized between the percentage overshoot and settling time. As the phase margin becomes larger, the amount and number of overshoots diminish 共Earni 关11兴兲. 4. Placing the zeros of the PID-Compensator very close to the origin increases the steady-state error, which is not acceptable. 5. The design has been optimized by running several trials of the program with different locations of the zeros of the PIDCompensator. Feed Forward Controller Design. The feedback controller can be designed using the conventional design tools like rootlocus and frequency domain techniques, whereas the feed forward controller cannot be designed similarly as the feed forward control loop is an open loop 共Stephanopoulos 关12兴兲. To illustrate how dynamic models can be used to design a feed forward control system, consider the block diagram shown in Fig. 2, which contains a single disturbance. Here the objective is to keep the liquid level in the GLCC constant. The disturbance is the inflow to the GLCC in the form of a slug. The time delay for the slug to travel from the slug detector to the GLCC is given by,
d⫽
Distance between the slug detector and GLCC Velocity of the Slug
(1)
Fig. 3 Closed loop magnitude plot for different compensator zero locations
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The transportation process can be assumed as a first-order transfer function as it is a pure time lag function. The final form of block 6 is given by, 1 G d共 s 兲 ⬵ G (2) d s⫹1 l where, D1 G l⫽ s The manipulated variable is the outflow which can be controlled by the liquid control valve, whose transfer function is given by Glcv and the pneumatic line transfer function is given by Gp1 . The incoming slug is characterized with a sensor whose transfer function is given by Gm2 . The information is sent to the pneumatic line and the liquid control valve through a feed forward controller Gc2 , to accommodate the slug. The error encountered due to slug characterization is taken care of by feedback controller Gcl . The liquid level set point is specified for both the feedback and feed forward controllers. Gsp is the set point tracker. From Fig. 2, the control loop output is given by, y 共 s 兲 ⫽G lc 共 s 兲 * G l 共 s 兲 * m 共 s 兲 ⫹G d 共 s 兲 * d 共 s 兲
(3)
where,
* 共 y s p ⫺G m1 * y 兲 ⫹G *pl G c2 * 共 G s*p y s p ⫺G m2 * d兲 m⫽G * pl G c1 Substituting equation 共4兲 in 共3兲 gives, y⫽
冋
册
(4)
* G* p1 G * 1c v G * v 1 共 G c1 ⫹G c2 G s p 兲 y sp * * * 1⫹ 共 G * G G G p1 1c v v 1 c1 G m1 兲
⫹
冋
册
* G *v 1 G *c2 G m2 兲 G d⫺共 G * pl G lc d. * * 1⫹ 共 G * G pl lc G * v 1 G c1 G m1 兲
(5)
To analyze the stability of the closed-loop system, one can consider the closed-loop transfer function in Eq. 共5兲. Setting the denominator equal to zero gives the characteristic equation,
* G *v 1 G *c1 G m1 兲 ⫽0 1⫹ 共 G * pl G lc
(6)
The roots of the characteristic equation completely determine the stability of the closed-loop system. Since the expression shown above is not a function of any of the feed forward control transfer functions, the feed forward controller has no effect on the stability of the feedback control system. This is a desirable situation, which allows the feedback and feed forward controllers to be tuned individually. The slug detector 共Block 7兲 is represented by the liquid flow rate transmitter gain 共4 –20 mA兲 and is given by,
共 20⫺4 兲 ⫻ 共 ⫺⌬Q 兲 Q max⫺Q min Taking the Laplace transform and simplifying,
⌬e⫽
(7)
⌬e 共 s 兲 ⫺ 共 20⫺4 兲 ⫽ (8) ⌬Q 共 s 兲 Q max⫺Q min The set point tracker 共Block 8兲 is specified in the controller design. The control mechanism should be capable of making the process output track exactly any changes in the set point 共i.e., y⫽ysp). This implies that the coefficient of ysp in Eq. 共5兲 should be equal to 1 and the set point tracker transfer function is given by, G m2 G sp ⫽ (9) Gd The feed forward controller transfer function 共Block 9兲 needs to be designed for the system. The controller should be capable of completely eliminating the impact of a slug on the liquid level. This implies that the coefficient of d in Eq. 共5兲 should be zero and the feed forward controller transfer function is given by, G m2 ⫽
G c2 ⫽
冉
Gd
* G *l G m2 G* pl G lc
冊
(10)
Control System Simulation. Based on the linear model and the designed controller, a simulator for the liquid level control system using liquid control valve is built in MATLAB/Simulink® 共Fig. 4兲. The outputs from the simulator, as shown in Fig. 5, are the liquid flow rate, liquid level and control valve position. The inflow disturbance 共unit slug兲 is 0.7 cft/s 共10773 bbl/d兲 with a slug velocity of 11 ft/sec 共3.35 m/sec兲. The liquid level in the GLCC with feedback control varies from ⫾6 ft 共1.83 m兲. The outflow rate overshoots to 0.72 cft/s and settles down in about 16 seconds. If the same disturbance 0.7 cft/s 共10773 bbl/d兲 is introduced to the integrated feedback and feed forward control system, the liquid level in GLCC varies between ⫾0.25 ft 共0.0762 m兲. No overshoot is observed in this case. The control valve dynamics are also negligible when compared to dynamics associated with feedback control system alone. Details of the simulator and simulation results are described in Earni 关11兴.
Experimental Program The experimental setup consists of a standard two-phase airwater flow metering loop, a slug generator, a GLCC test section and a slug detection system. Slug lengths varying from 20– 800 pipe diameters can be generated using the slug generator. The slug generator consists of a 9-gallon 共34.07 Liters兲 metallic tank with a level indicator. Three pneumatic 2-in 共5.08 cm兲 ball valves are
Fig. 4 Liquid level control simulator „Matlab-Simulink… with both feedback and feedforward control loops
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Fig. 5 Liquid levelÕliquid inflow-outflow response for a unit slug input „VsÄ11 ftÕs, LÄ22 ft… PÄ0.08, DÄ0.03
connected to this tank. The GLCC body is a 3-in 共7.62 cm兲 transparent PVC pipe with a modular 3-in inclined aluminum tangential inlet. The inlet slot area is 25% of the cross sectional area of the inlet pipe. The total height of the GLCC is 7 ft 共2.13 m兲, which is divided by the inlet into the lower liquid section and the upper gas section. The liquid leg is a 2-in gray PVC pipe with a liquid control valve. To measure the liquid level, the GLCC is equipped with a differential pressure transducer. The gas leg is also a 2-in gray PVC pipe with a gas control valve and an absolute pressure transducer to measure the GLCC pressure. The outlet section is constructed to both recombine the gas and liquid legs 共metering loop application兲 or to separate the gas and liquid streams 共bulk separation application兲 corresponding to the application. The recombination point is 6 inch 共15.4 cm兲 below the inlet plane, which helps to self-regulate the liquid level of the GLCC for operating without any control system. For full separation configuration, the liquid and the gas outlets are separated and a suitable control system should be used for proper operation. A slug detection section, which is 22-ft 共6.71 m兲 away from the GLCC, is set up comprising of two conductance probes 共C1 and C2兲, as shown in Fig. 6. There is provision to alter the distance between the two probes. For fully developed slug flow the expected slug length is between 20d–30d 共Taitel & Barnea 关13兴兲. In order to ensure accurate measurement of slug, it is necessary to guarantee a maximum of only one slug in between. Hence a more precise slug detection is accomplished when a minimum distance of 8.5 pipe diameters separates the two probes. Conductance probes are used to characterize and measure the length of the slug at upstream of the GLCC. The voltage from the probes is scaled from 0–10 volts. In other words, when no liquid is in contact with probe, or liquid does not bridge the negative ends simultaneously, 0 volt is measured. The slug detection can be classified by the following stages: • Transmission of the current to the conductance probes and receiving the voltage signals from the probes. Current is sent to the probes and the voltage across the probe is measured, which is a function of the wetted area of the probe. • Conditioning (amplification and anti-aliasing filtering) of signals from the probes. The voltage signals are scaled from 0–10 volts. This causes some events 共related to small gas Journal of Energy Resources Technology
bubbles or small liquid droplets兲 to be eliminated from the original signal. In order to filter out these spurious signals they are sent through a median filter. • A/D Conversion: The filtered signals are digitized based upon a threshold voltage 共i.e. if the voltage from the sensor is greater than or equal to the threshold voltage兲, in order to eliminate small slugs. The effect of small slugs is handled by feedback control system alone. • Signal Processing and Analysis: The digital signals are then processed to obtain the slug velocity and length. • Slug Characterization: Transmission of the processed information to the feed forward control system. Three different control strategies are configured and tested to evaluate the performance of each strategy under different flow conditions and with different lengths of dumped slugs. It may be noted that for the different control strategies that were tested, the pressure is always feedback controlled by GCV and the control configuration varied only for level control. These strategies are feedback level control by LCV, feed forward level control by LCV and integrated feedback and feed forward level control by LCV. Two sample cases of flow conditions are described below as typical examples, to evaluate the effectiveness of the integrated feed forward and feedback control system. Case 1—Evaluation of the system response for a unit severe slug superimposed on a normal stratified flow A unit liquid slug is introduced to the system using the slug generator. The initial liquid superficial velocity is V sl ⫽0.38 ft/s 共0.115 m/s兲, and the gas superficial velocity is V sg ⫽3.84 ft/s 共1.17 m/s兲, and with the slug generator, adjusting the timer appropriately, a unit slug of 12-ft
Fig. 6 Schematic of the slug detection zone with two conductances probes „C1, C2…
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共3.66 m兲 length is dumped. In this process the time responses of the slug detector probes, liquid level and LCV position are measured by the appropriate sensors. The data are acquired and plotted for different control configurations 共feedback control alone and integrated feedback feed forward control systems兲 as shown in Fig. 7. In the case of the feedback control alone the liquid level overshoot is around 54 inches 共1.372 m兲 and level settles to around the set point (25 inch⫾5 inch) after 6.4 seconds. The control valve and liquid level dynamics are also estimated by calculating the RMS value. The slug associated liquid level RMS value is around 17 inches 共43.18 cm兲 and RMS value of the LCV dynamics is 15. When one considers the feed forward control loop alone, the liquid level in the GLCC shoots up to about 48 inches 共121.92 cm兲 and settles at a different position. It can be intuitively expected that the feed forward control system alone cannot bring the level to the set point level, as it does not have the information about the set point. However, with integrated feedback and feed forward control system, the liquid level overshoot is only 38 inches 共96.52 cm兲 and the liquid level is brought back to the set point level in only about 5 seconds. The liquid level RMS value is around 7.33 inches 共18.62 cm兲 and RMS value of the LCV dynamics is 11. It may also be noted that the liquid control valve saturates at full open position for a shorter period of time for integrated control loop compared to the feedback control alone. Case 2—Evaluation of the system response for a unit severe slug superimposed on a normal slug flow: The other flow condi-
tion for which the predictive control strategy is tested is the normal slug flow condition. A long slug, ranging from 20–25 ft 共6.1– 7.62 m兲, is dumped into the system, which is already operating under slugging conditions. The results are shown in Fig. 8. A unit slug of length 25 ft 共7.62 m兲 is dumped into the system, which is operating at V sl ⫽0.41 ft/s 共0.125 m/s兲, and V sg ⫽6.61 ft/s 共2.015 m/s兲. The system performance is evaluated for feedback control alone and integrated feedback-feed forward control system. In the case of feedback control system the maximum liquid level overshoot in the GLCC is around 115 inches 共2.92 m兲 whereas it is only 100 inches 共2.54 m兲 with the integrated feedback-feed forward control system. The settling time for the liquid level to come back to the set point level is 36 seconds for the former, while it is only around 17 seconds for the latter. The liquid level dynamics are evaluated by calculating the RMS value of the liquid level readings associated with the slug, which is around 42 inches 共106.7 cm兲 for without feed forward case and for the case with feed forward control system it is only 32 inches 共81.3 cm兲. Similarly, the total control valve dynamics are also evaluated based on its RMS value, which is calculated to be 17 for the feedback control system, and it is 11 for the integrated feedback-feed forward control system. Table 1 gives a summary of all the operational conditions for which the control strategy is tested.
Fig. 7 a-System response for feedback control system „VsgÄ3.84 ftÕs, VslÄ0.38 ftÕs, slug lengthÄ12 ft… b-System response for the integrated feed forward and feedback control systems „VsgÄ3.84 ftÕs, VslÄ0.38 ftÕs, slug lengthÄ12 ft…
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Fig. 8 a-System response for feedback control system „VsgÄ6.61 ftÕs, VslÄ0.41 ftÕs, slug lengthÄ25 ft… b-System response for integrated feed forward and feedback control system „VsgÄ6.61 ftÕs, VslÄ0.41 ftÕs, slug lengthÄ25 ft…
Conclusions A strategy for predictive control of compact separators has been developed based upon the principle of slug detection. This is a non-intrusive technique unlike other more traditional ways of slug handling. When severe slugs are encountered, the feed forward
control system takes the lead by manipulating and timing the control valve position appropriately to negate the effect of the slug. The feedback control system takes care of small variations in the flow and maintains the set point liquid level. The conclusions from this investigation are summarized below.
Table 1 Summary of the Results for Feedback Control System and Integrated Feedback & Feed forward Control Systems
S. No.
Vsg ft/sec
Vsl ft/sec
1 2 3
3.84 5.54 3.65
0.3 0.3 0.4
4
6.61
0.41
5
6.61
0.83
6
7.27
0.55
7
8.4
0.55
8
11.1
0.55
Feedback Control
Feedback forward Control
Slug Dumped 共Ft兲
⬙ of H20
Liq Lvl
Settling Time 共Rise Time兲
Liq Lev RMS
LCV Dynamics RMS
⬙ of H20
Liq Lvl
Settling Time 共Rise Time兲
Liq Lev RMS
LCV Dynamics RMS
12 19 21 18 17 15 25 25 12 22 21 19 21 19
54 83 106 98 96 79 115 109 74 97 85 87 93 110
6.4共2.05兲 14.5共3.75兲 26共3.2兲 23.65共3.1兲 28.5共3.75兲 21.15共2.75兲 35.8共3.85兲 50共3.8兲 22.2共3.65兲 23.6共2.45兲 22.4共4.6兲 15.2共2.6兲 22共2.32兲 24.1共2.5兲
16.97 32.58 33.54 30.22 29.32 26.2 41.89 44.56 27.32 36.57 36.78 30.25 33.12 47.35
14.83 14.57 13.46 15.67 14.32 8.67 16.83 14.52 16.53 14.28 14.13 8.69 8.71 9.44
38.12 52 61 53 50 56 100 109 52 67.5 62.5 78 86 98
5共2.6兲 7.85共1.5兲 6.2共2.92兲 5.75共2.3兲 7.86共2.65兲 7.2共2.95兲 17.3共2.8兲 26共4.8兲 5.9共2.1兲 12.6共1.75兲 14.35共1.9兲 5.3共1.7兲 9.4共2.3兲 7.15共1.5兲
7.33 14.82 17.03 15.05 16.68 16.93 32.66 31.98 16.98 18.29 15.08 24.65 25.32 32.29
11.07 9.82 9.8 9.8 10.1 13.92 10.85 12.38 12.17 10.23 13.76 12.27 15.92 11.45
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1. A mathematical model is developed for GLCC predictive liquid level control system. 2. As feed forward control is an open loop system, the conventional design methods cannot be used. Hence, an analytical method of control system design is adopted for designing the feed forward controller. Addition of a feed forward control loop does not affect the stability of the existing feedback control system. From the analytical design, it can be noted that the feed forward controller settings are a function of the slug velocity. Even though for a perfect feed forward control, different settings have to be deployed for different slug velocities, the controller settings designed for one particular slug velocity can be used for a range of velocities with satisfactory performance. 3. Integrated feedback and feed forward liquid level control system simulators were developed using MATLAB/Simulink® software. Detailed dynamic simulations show that the predictive liquid level control system can handle long slugs without affecting the operational efficiency of GLCC by maintaining the liquid level close to the set point level, which is otherwise not possible with a feedback control system alone. 4. A state-of-the-art experimental facility was designed and constructed to study the GLCC performance for the different control strategies. Detailed experimental studies demonstrate that slug detection has to be exact for perfect feed forward control. However, good performance can be obtained with feed forward control system if it is integrated with the feedback control system to compensate for the error associated with the slug detection. Maintaining the liquid level close to the set point level reduces the liquid carry over 共LCO兲 and gas carry under 共GCU兲 considerably. The control valve dynamics are also reduced enhancing the control valve life. However, if there is a significant error in slug characterization, it will lead to larger control valve dynamics in order to achieve good level control. Thus the experimental results obtained demonstrate that the proposed strategy is a viable approach for GLCC predictive control.
Acknowledgments The authors wish to thank Tulsa University Separation Technology Projects 共TUSTP兲 member companies for supporting this project. The authors also acknowledge the National Petroleum Technology Office 共NPTO兲, U.S. Department of Energy for the Grant: DE-FG26-97BC15024.
Nomenclature d D1 e G GCV LC LCV PC Q RMS s V
⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽
flow disturbance constant from GLCC geometry error signal sub-system transfer function gas control valve liquid level controller liquid control valve pressure controller volumetric flow rate 共ft3/s兲 root mean square Laplace variable velocity 共ft/s兲
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y ⫽ output w ⫽ Frequency 共Hz兲 z ⫽ zeros of the controller Greek Letters
⫽ time constant 共s兲 ⌬ ⫽ incremental deviation Subscripts c d FB FF lcv max min m pl s sg sl sp vl
⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽
controller, crossover time delay Feedback Control feed forward Control liquid control valve maximum minimum sensors pneumatic line slug superficial gas superficial liquid set point volume to level
References 关1兴 Marrelli, J. D., Tallet, M., Yocum, B., Dunbar, D., Mohan, R. S., and Shoham, O., 2000, ‘‘Methods for Optimal Matching of Separation and Metering Facilities for Performance, Cost, and Size: Practical Examples from Duri Area 10 Expansion’’ ETCE00-ER-10165, proceedings of the ETCE 2000 Conference of ASME Petroleum Division of ASME Petroleum Division, February 14 –17, 2000. 关2兴 Andreussi, P., Bendiksen, K. H., and Nydal, O. J., 1993, ‘‘Void Distribution in Slug Flow,’’ Int. J. Multiphase Flow, 19共5兲, pp. 817– 828. 关3兴 Dhulesia, H., Bernicot, M., and Romanet, T., 1995, ‘‘Field Installation of an Acoustic Slug-Detection System,’’ presented at the SPE Annual Technical Conference and Exhibition, Dallas, October 22–25, pp. 285–291. 关4兴 Barnea, D., Shoham, O., Taitel, Y., and Dukler, A. E., 1980, ‘‘Flow Pattern Characterization for Two-Phase Flow by Electrical Conductance Probe,’’ Int. J. Multiphase Flow, 6, pp. 387–397. 关5兴 Wang, S., 1997, ‘‘Control System Analysis of Gas-Liquid Cylindrical Cyclone Separators,’’ M.S. thesis, The University of Tulsa. 关6兴 Mohan, R., Wang, S., Shoham, O., and Kouba, G., 1998, ‘‘Design and Performance of Passive Control System for Gas-Liquid Cylindrical Cyclone Separators,’’ ASME J. Energy Resour. Technol., 120共1兲, pp. 49–55. 关7兴 Wang, S., Mohan, R. S., Shoham, O., Marrelli, J. D., and Kouba, G. E., 2000, ‘‘Performance Improvement of Gas Liquid Cylindrical Cyclone Separators Using Integrated Liquid Level and Pressure Control Systems,’’ ASME J. Energy Resour. Technol., 122, pp. 185–192. 关8兴 Wang, S., Mohan, R., Shoham, O., Marrelli, J., and Kouba, G., 2000, ‘‘Control System Simulators for Gas-Liquid Cylindrical Cyclone Separators,’’ ASME J. Energy Resour. Technol., 122, pp. 177–184. 关9兴 Wang, S., 2000, ‘‘Dynamic Simulation, Experimental Investigation and Control System Design of Gas-Liquid Cylindrical Cyclone Separators,’’ Ph.D. dissertation, The University of Tulsa. 关10兴 Payne, R. L., Huff, R. E., and Ogren, W. E. 1996, ‘‘Slug Flow Mitigation Control System and Method,’’ U.S. Patent 5544672. 关11兴 Earni, B. S., 2001, ‘‘Predictive Control of Gas-Liquid Cylindrical Cyclone 共GLCC兲 Separators,’’ M.S. thesis, The University of Tulsa. 关12兴 Stephanopoulos, G., 1984, ‘‘Chemical Process Control,’’ PTR Prentice Hall International Series in the Physical and Chemical Engineering Sciences. 关13兴 Taitel, Y., and Barnea, D., 1990, ‘‘Two-phase Slug Flow,’’ Academic Press, Inc. 关14兴 Norman S. Nise, 2000, ‘‘Control System Engineering,’’ 3rd Edition Benjamin/ Cummings Publishing Company, Inc.
Transactions of the ASME
Shankar Earni is a Doctoral student at Oklahoma State University and he has MS in Mechanical Engineering from University of Tulsa. His research interests include Manufacturing, Process Control and Product Development.
Shoubo Wang is a Visiting Assistant Professor at The University of Tulsa, 600 South College Avenue Tulsa, OK 74104, e-mail:
[email protected]. He received his Ph.D. and M.S. degrees in Petroleum Engineering from the University of Tulsa, and his B.Sc. degree in Mechanical Engineering from University of Petroleum, China. Wang teaches and conducts research in the areas of multiphase flow in pipes, multiphase compact separators, instrumentation & process control, and control system design. He served as the vice-chair of the Manufacturing and Services Symposium of the Engineering Technology Conference on Energy (ETCE-2002) of ASME Petroleum Division.
Ram S. Mohan is an Associate Professor of Mechanical Engineering and the associate director of the Separation Technology Projects (TUSTP) at the University of Tulsa, 600 South College Avenue Tulsa, OK 74104, e-mail:
[email protected]. He received his Ph.D. and M.S. Degrees in Mechanical Engineering from the University of Kentucky, and his B.Sc. (Engg.) degree in Mechanical Engineering from the University of Kerala, India. Mohan teaches and conducts research in the areas of control system design, compact separators and manufacturing processes. He served as the chair of the Manufacturing and Services Symposium of the Engineering Technology Conference on Energy (ETCE-2001 and ETCE-2002) of ASME Petroleum Division.
Ovadia Shoham is professor of petroleum engineering at the University of Tulsa. He received his Ph.D. degree in Mechanical Engineering from Tel Aviv University and his M.S. and B.S. degrees in Chemical Engineering from the University of Houston and the Technion in Israel. Shoham teaches and conducts research in the area of modeling two-phase flow in pipes and its application in the oil and gas production, transportation and separation. He served as the associated director and the director of research of the Tulsa University Fluid Flow Projects for 10 years. Since 1994 Dr. Shoham directs the Tulsa University Separation Technology Projects (TUSTP), conducting research on compact separators. He has taught short courses on multiphase flow for various oil and gas companies worldwide. Shoham has authored or co-authored more than 90 publications in the areas of multiphase flow and separation and production operations. He was a member of the SPE Production Operation Technical Committee (1990–1992 and 1998–2000) and received the SPE Cedric K. Ferguson award in 1999. Jack Marrelli is a Senior Research Associate at the Production & Facility Optimization Team of ChevronTexaco, Houston, TX 77082-6696, email:
[email protected]. His research interests include compact separation, instrumentation, controls, multiphase flow, data analysis and multiphase metering. Marrelli holds B.S. and M.S. degrees in Electrical Engineering from University of Connecticut, Storres, CT and a Ph.D. in Bioengineering from the University of Connecticut.
Journal of Energy Resources Technology
JUNE 2003, Vol. 125 Õ 153
