adaptive control technique a solution fortustp.org/publications/vasu isa03-p163.pdf · adaptive...

Copyright 2003 by ISA – The Instrument, Systems and Automation Society. Presented at ISA EXPO 2003; http:/www.isa.org

ADAPTIVE CONTROL TECHNIQUE A SOLUTION FOR GLCC SEPARATORS

Vasudevan Sampath, M.S.* Tulsa University Separation Technology Shoubo Wang, Ph.D.** Projects (TUSTP) Ram Mohan, Ph.D.** and 600, S. College Ave., Ovadia Shoham, Ph.D.** The University of Tulsa Departments of Petroleum and Tulsa, Oklahoma 74104 Mechanical Engineering [email protected] * Member, ASME ** Member, ASME and SPE KEYWORDS Adaptive Control, Gas Liquid Cylindrical Cyclones, Optimal Control ABSTRACT Field applications of Gas Liquid Cylindrical Cyclone (GLCC) separators strongly depend on the implementation of control systems, due to its compactness, less residence time and possible inlet large flow variations. In this study an adaptive control algorithm is identified for GLCC separator control using a hardware controller and tested extensively. A new optimal control strategy is developed for GLCC separators to maintain the liquid level inside the GLCC with minimum dynamics of liquid control valve. Dynamic simulation for GLCC separators equipped with the new optimal control strategy is conducted for different inflow scenarios using MATLAB/Simulink software. EXACT – Expert Adaptive Control Technique, an inbuilt adaptive tuning algorithm in Foxboro 762CNA micro controller is studied in detail for application to GLCC separator control system applications. Detailed experimental investigations are conducted to evaluate system sensitivity and dynamic behavior for the proposed new optimal control strategy and adaptive control. The results demonstrate that the optimal control strategy with adaptive control is capable of controlling the liquid level and reducing the dynamics of the liquid control valve significantly. The results of theoretical and experimental studies provide GLCC control with robust hardware controller capable of adapting to changes in flow conditions, which can be readily deployed in the field.


INTRODUCTION Separation technology in petroleum industry has been progressing slowly for several decades. But increasing operational problems and economic pressures have driven petroleum companies to seek alternative solutions to separation processes using conventional separators. Conventional separators have been used in the past for several decades and are still being used for separation. A new generation of compact separators called Gas-Liquid Cylindrical Cyclone (GLCC) has become increasingly popular and attractive alternatives to conventional separators. Significant advantages of GLCCs are its compactness, ease of operation, smaller footprint, lower cost and lower weight. The total number of GLCCs installed in the field in 1999 –2000 was about 150 and the current figures show about 400 GLCCs in the field. GLCC separator is a vertically installed pipe, mounted with a downward inclined tangential inlet. The two phases of the incoming mixture are separated due to the centrifugal/buoyancy forces produced by the swirling motion. The heavier fluid (liquid) is forced radially towards the wall and is collected from the bottom, while the gas, which is a lighter fluid moves to the center and flows out from the top.

Fig. 1 - GLCC Schematic Diagram The liquid carry-over to the gas stream is termed as LCO (Liquid Carry-Over) and the gas carry-under into the liquid stream is termed as GCU (Gas Carry-Under). Liquid carry-over and gas carry-under percentages can be either controlled or avoided by implementing appropriate control systems. Thus, GLCC separation efficiency can be vastly improved for large flow variations through suitable application of control strategy.


Design of new optimal control strategy for minimizing the dynamics of Liquid Control Valve (LCV) is presented along with the implementation of a unique adaptive control algorithm called EXACT control, which is an embedded design of the hardware controller chosen for this study. GLCC EQUIPPED WITH CONTROL SYSTEM In order to analyze a system, as complex as the one for GLCC separator, a mathematical model representing the system is initially formed. This mathematical model is then transformed to transfer functions in the control system block diagrams for analysis and simulation. For this control system study the controlling parameters are identified as the liquid level and pressure in the GLCC separator and liquid and gas control valve positions. In this study, the main focus is on feedback control systems. Pressure transducer measured the pressure and a differential pressure transducer, which is indicated as level transducer in Figure 2, measured the liquid level. The position of gas and liquid control valve is measured by HART Tri- loop sensor. The liquid level control can be achieved by the direct operation of Liquid Control Valve (LCV) or by the indirect pressurization of GLCC separator by operating Gas Control Valve (GCV). Pressure inside GLCC separator can be controlled by the direct operation of GCV. The level transducer senses the liquid level and this signal is sent to the controller as the process variable. Controller compares the actual liquid level to the set point liquid level and operates the LCV/GCV to control the liquid level. Similarly the controller operates on the pressure signal from the pressure transducer to manipulate the GCV for pressure control. For gas-dominated system, the liquid level control by LCV can be used. For liquid dominated system, a gas control valve on the gas leg can be used to control the liquid level (Wang 2000). The integrated control strategy can be applied to cases with slug flow condition, as it provides an immediate action on liquid level by both LCV and GCV. For bulk separation applications, liquid level control by LCV and pressure control by GCV, as two independent control loops, is desirable. Optimal control strategy is implemented under severe slugging flow and where the control valve dynamics is to be limited to increase the life of control valve. Thus, based on operating conditions and applications, the following control strategies could be adopted, namely:

1. Liquid level control by liquid control va lve (LCV). 2. Liquid level control by gas control valve (GCV). 3. Pressure control by gas control valve (GCV). 4. Integrated liquid level control by both LCV and GCV. 5. Integrated liquid level control by LCV and pressure control by GCV. 6. Optimal control–liquid level control by LCV and LCV position control by GCV. 7. Optimal control–liquid level control by GCV and GCV position control by LCV.

The first six of the existing feedback control strategies mentioned above have been designed, implemented and tested successfully by Wang (2000). Following are the general steps involved in the development of control system and dynamic simulator (Wang 2000):

1. Transformation of physical system into block diagram: The GLCC separator, as shown in Figure 2, consists of electric, pneumatic and electro mechanical devices. Devices like control valves, actuators, pressure transducers, level transducers and pneumatic transmitters are


converted to equivalent block diagram models. Certain degree of approximations is applied to obtain simple, yet useful mathematical representation of the real system.

2. Mathematical model for schematic: Linear model of each subsystem or component is achieved by making reasonable and realistic assumptions. Laplace transform of each subsystem is derived from the linear differential model.

3. Transfer function representation of complete subsystem: In order to evaluate the system response, the mathematical model represented as block diagram in the previous step is transformed to transfer functions. Every single subsystem is represented by a transfer function that relates its input to its respective output. In the feedback control system as complex as this, the closed loop transfer function is too difficult to obtain, but for this study, the open loop transfer function is taken into consideration.

4. Analysis and design: In this step "Root Locus Analysis" (Nise, 1992), is performed on the open loop transfer function. This is a very powerful tool for analyzing and designing control systems. This technique provides a graphical method of plotting the locus of the roots in the s-plane, as the system gain is varied over a complete range of values. The roots corresponding to a particular value of system parameters can be located on the locus. Using this root locus technique the controller parameters, such as P, I and D, can be designed for the desired characteristic response. Once the complete system is represented by a single transfer function, characteristics, such as transient response, steady-state error, stability and sensitivity, can be analyzed and designed.

Fig. 2 – Schematic of GLCC Equipped with Control System

Multiphase Flow

Controller

Liquid Stream

GCV

LCV

Pressure Transducer

Level Transducer

Gas Stream

GCV position signal

LCV position signal


OPTIMAL CONTROL STRATEGIES The simple control strategies and integrated control strategies listed above are primarily composed of simple PID controllers working on the process variable, which is assumed to be independent. However, these strategies are not robust enough to handle wide range of operating conditions and may fail because of the system dynamics caused due to the interaction of pressure and the liquid level inside GLCC separator. This factor was not considered in the design of these controllers. Where as the optimal control strategy is capable of handling this interaction of liquid level and pressure, minimizing the operating pressure, providing unique valve positions for a given flow condition. There are two optimal control strategies developed, optimal control with LCV controller as the master controller and optimal control with GCV controller as the master controller. 1. GLCC Optimal Control Strategy with LCV Controller as Master Controller The objective of this optimal control strategy is to minimize the pressure, maximize the liquid flow for any given inflow condition and optimize the control valve performance. This is achieved by controlling the liquid-valve at an optimum position. This optimum position serves as the set point for the GCV controller. The most important application of this control strategy is that it can operate in a wide range of liquid and gas flow condition with minimum GLCC pressure. Optimal control strategy has two different controllers. The liquid level is controlled using the LCV controller and the GCV controller controls the optimum position of Liquid Control Valve (LCV). Wang (2000) carried out extensive work on this optimal control strategy. The main drawback observed and reported on this optimal control strategy is the dynamics in operation of the LCV. Since LCV is the master valve operating on the liquid level and it also being the load-bearing valve, due to its dynamics, the life expectancy of this valve is reduced. This is one of the most important reasons why the second optimal control strategy is developed. 2. GLCC Optimal Control Strategy with GCV Controller as Master Controller The objective of this control strategy, in addition to minimizing pressure and maximizing flow through the control valve, is also to minimize the dynamics of the liquid control valve (LCV). The main load bearing control valve is the liquid control valve and not the gas control valve. Therefore it is preferable to have GCV with more dynamics than liquid control valve. This optimal control strategy aims at achieving this. In this control strategy the liquid level is controlled by the backpressure using the GCV controller as the master controller and the LCV controller acting on the position of the GCV as the slave controller. Master controller, has the liquid level measured by a differential pressure transducer as the process variable. Master controller maintains the liquid level to the user set point liquid level by operating the GCV. The position of the gas control valve (GCV) is sensed by the hart-tri loop sensor and the signal is fed to the slave controller. Slave controller maintains the position of the GCV around the set point (optimum position), thereby minimizing the pressure inside the GLCC separator, and at the same time maintaining the liquid level. The optimum position of a GCV is defined as the position of GCV that reduces the dynamics of LCV and results in a very low-pressure drop across the LCV. This optimum position is the set point for the LCV slave controller. If the GCV changes its position


from the optimum position the slave controller operates LCV in-order to bring the GCV back to its optimum position. Figure 3 below shows a schematic of this optimal control strategy.

Fig. 3- Schematic of Optimal Control Strategy with GCV Master Controller

The master control loop consists of the gas control valve on the gas leg, a liquid level sensor (such as a differential pressure transducer) and a PID controller. The liquid level signal sent to the PID controller is maintained around the user set point by correspondingly opening or closing the GCV by this controller. Thus, the intention of the master control loop using the GCV is to maintain the liquid level around its set point. The slave control loop consists of the liquid control valve in the liquid leg, gas control valve position sensor (Hart tri- loop valve position indicator) and PID controller. The gas control valve position sensor sends a signal to the PID controller, which drives the LCV. The central concept in this control strategy is to maintain the liquid level with minimal pressure inside the GLCC and by smooth operation of the LCV. Dynamic Simulation

A MATLAB/Simulink model is created for optimal control strategy with GCV controller as master controller, using the block diagram shown in Figure 4. The details of relations shown in this figure can

Liquid Stream

GCV

LCV

Pressure Transducer

Level Transducer

Gas Stream

GCV position signal

LCV Slave Controller

GCV Master Controller

Multiphase Flow


be referenced in Sampath (2003). This model is tested for different cases of liquid and gas inflow into the GLCC separator and the result of one such case is presented in Figure 5.

a: LCV Position (% Open) b: GCV Position (%Open)

Fig. 4 – Block diagram of Optimal Control Strategy with GCV Master Controller

CONTROLLER

GAS RATE IN

SETPOINT

TRANSMITTER/SENSOR

RELATION 4

+ GCV &ACTUATOR

GASRATE

OUT

LIQUID RATE IN

+−− LIQUIDRATE OUT

RELATION 2

PGLCC

RELATION 1 RELATION 3

+−

LIQUID LEVEL

+

LCV CONTROLLER LCV

+RELATION 6

PNEUMATIC LINE&ACTUATOR RELATION 1

−+

GCV POSITION SENSOR

GCV SETPOINT POSITION

RELATION 7

GLCC

PRESSURE

RELATION 6

−−

RELATION 5−−

CONTROLLER

GAS RATE IN

SETPOINT

TRANSMITTER/SENSOR

RELATION 4

+ GCV &ACTUATOR

GASRATE

OUT

LIQUID RATE IN

+−− LIQUIDRATE OUT

RELATION 2

PGLCC

RELATION 1 RELATION 3

+−

LIQUID LEVEL

+

LCV CONTROLLER LCV

+RELATION 6

PNEUMATIC LINE&ACTUATOR RELATION 1

−+

GCV POSITION SENSOR

GCV SETPOINT POSITION

RELATION 7

GLCC

PRESSURE

RELATION 6

−−

RELATION 5−−


c: Liquid Level in GLCC (in ft) d: Pressure inside GLCC (psi)

Fig. 5- Step Response for Optimal Control GCV Master Controller sftQL /06.0 3−=∆ , sftQG /5.0 3=∆ )

The results of the simulation show that the dynamics of LCV has been reduced considerably compared to the simulation results of the other control strategies. IMPLEMENTATION OF ADAPTIVE CONTROL The performance of GLCC separators can be improved by eliminating liquid over flow into the gas leg or gas blow out through the liquid leg, utilizing suitable control strategy as described above. Most of the hardware controllers manufactured these days are microprocessor-based controllers. One of the most important features of hardware controller exploited for GLCC separator control system is adaptive control. The flow variations at the upstream of GLCC may cause abrupt changes in liquid level, which might be difficult for a conventional controller with a single PID setting. In order to achieve a better control of the liquid level or the process variable, to accommodate the different flow conditions, adaptive control technique is used. Adaptive control algorithm in the hardware controller continuously monitors the process variable and updates the controller PID parameters based on the dynamics of the process variable. The hardware controller chosen for testing on GLCC separator platform for various control strategies was a Foxboro 762CNA micro controller with EXACT adaptive tuning algorithm. This controller is based on analysis of the transient response of the closed loop system to set point changes or load disturbances and traditional tuning methods in the Ziegler-Nichols (1942) spirit. The idea behind the algorithm is a pattern recognition approach published by Kraus and Myron (1984). This EXACT adaptive control system is based on the determination of dynamic characteristics from a transient, which results in sufficiently large error. If the controller parameters are reasonable, a transient error response of the type shown in Figure 6 is obtained. Heuristic logic is used to detect whether a proper disturbance has occurred and to detect the peaks E1,E2, and E3 and period Tp. The


heuristic logic used is outlined in Figure 7. The estimation process is simple. It is, however, based on the assumption that the disturbances are steps or short pulses.

Fig. 6- Typical Response of Control Error to Load Change Fig. 7- Heuristic Logic used in the EXACT Control System to Determine the Characteristics of a

Closed-loop Transient

Time

Period (Tp)

E1

E2

E3

E r r o r

ADAPT

Settle Quiet

Locate PK 1

Locate PK 3

Verify PK 2

Locate PK 2

Verify PK 1

Verify PK 3

Third Peak Found

Third Peak Not Found

Second Peak Not Found


EXACT control requires that the loop is closed with a controller that gives a reasonably stable response. In case of GLCC separator, the designed PID settings offer reasonable control till the process upset takes place. Even in the worst case scenario the process is still under control, but with larger dynamics of the process variable and control valve positions. The tuning procedure requires prior user parameter inputs including controller parameters like, P, I and D. It also requires information of the time scale of the process. This is used to determine the maximum time the heuristic logic, described in the above Figure 7, waits for the second peak. Some measure of the process noise is also needed to determine that a disturbance has occurred and to set the tolerances in the heuristic logic. The damping and overshoot determined by the algorithm is compared with user parameter input, damping and overshoot. Decision on whether the controller parameter has to be newly calculated is determined from the comparison of damping and overshoots from the process with those values from the user parameters. If the damping ratio and overshoot calculated from the process transients are less than those values that user inputs, then the algorithm does not calculate the new P, I and D. If the damping ratio and overshoot values calculated from the process transients is more than the values input by the user, then new P, I and D values are calculated and used by the controller. EXPERIMENTAL INVESTIGATION All the control strategies presented in the paper are implemented and tested on a 2” GLCC with Foxboro 762CNA micro controller in EXACT configuration. Figure 8 shows the result of implementation of optimal control strategy with GCV master controller. The operation of LCV, the load bearing control valve is observed to be very smooth.

Fig. 8-Optimal Control Strategy with GCV Master Controller

Optimal Control -GCV Master Controller

0

10

20

30

40

50

60

70

80

90

100

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Time (units 0.05 secs)

Liq

uid

Lev

el(in

ch o

f wat

er),

Pre

ssu

re (P

sia)

, LC

V (%

clo

se),

GC

V (%

clo

se)

Pressure

Liquid LevelGCV PositionLCV Position

0.55 to 2.2 ft/sec 1.3 ft/sec 2.2 ft/sec

Liquid Flow Rate Surge ConditionVsl=0.55 to 2.2ft/sec, 1.3 ft/sec, 2.2 ft/sec, Vsg=5.5 ft/sec


Fig. 9- PID vs. EXACT adaptive control – change in gas flow conditions 30 – 40 ft/sec.

The dynamic response of the system for change in gas flow conditions from 30- 40 ft/sec. is shown in Figure 9. First 2 minutes of the data represent the system with PID controller and the next 3 minutes represent the system with EXACT controller. This plot typically shows the decrease in control valve dynamics for the same liquid level range. Thus the conclusion is that EXACT control in addition to the optimal control strategy can aid in reducing the control valve dynamics and there by increasing the life of the valves. CONCLUSIONS Based on the results obtained from dynamics simulation of the new optimal control strategy and experimental investigation of control strategies and adaptive control implementation, following conclusions can be drawn: • The new optimal control strategy can handle any combination of gas and liquid flow disturbances,

including slug flow. However, the set point position for the gas control valve may not be optimum for all flow conditions. Extreme flow conditions may cause large dynamics to liquid level and control valve positions. The dynamics can be controlled by appropriately changing the set point position of the gas control valve.

• The dynamics of the load bearing control valve, which is the liquid control valve is reduced

drastically. For every flow condition there is a unique position for this control valve and the transition from one position to another for change in liquid flow rate is smooth. Thus, the new optimal control strategy with adaptive control increases the life of the liquid control valve.

Change in Gas Flow ConditionsVsg =30ft/sec to 40ft/sec, Vsl=0.55ft/sec

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000

Time (unit 0.05 Secs)

Liq

uid

Lev

el (

inch

of

H20

), P

ress

ure

(p

sia)

, LC

V (

%cl

ose

)

GLCC Pressure

Liquid Level

LCV Position

PID EXACT

30ft/sec 40ft/sec40ft/sec 30ft/sec

Initial Values:P=4.39I=0.02 repeat/minD=0.07 min

Final Values:P=1.45I=0.01 repeat/minD=0.001 min

EXACT Control vs. PID Control


• Using adaptive control the GLCC separator system can now start with a designed PID value and adapt it self to various flow conditions minimizing the dynamics of the process variable and control valve.

NOMENCLATURE

( )sPID = PID compensator transfer function Q = Volumetric flow rate, L3/t, /sft 3 s = Laplace variable t = Time, t, seconds

sgV = Superficial gas velocity, L/t, ft/s

slV = Superficial liquid velocity, L/t, ft/s ? = Incremental deviation G = Gas L = Liquid

REFERENCES 1. Kraus, T. and Myron, T.: "Self-Tuning PID Controller Uses Pattern Recognition Approach,"

Control Engineering, June 1984. 2. Nise, N.S.: “Control System Engineering,” Benjamin/Cummings Publishing Company, Inc. 1992. 3. Sampath, V: “Design and Development of Adaptive Control for Gas Liquid Cylindrical Cyclone

Separators,” M.S. Thesis, The University of Tulsa, 2003. 4. Wang, S., Mohan, R. and Shoham, O.: "Dynamic Simulation and Control System Design for Gas-

Liquid Cylindrical Cyclone Separators," SPE 49175, SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, Sep. 27-30, 1998.

5. Wang, S.: "Dynamic Simulation, Experimental Investigation and Control System Design of Gas-

Liquid Cylindrical Cyclone Separators," Ph.D. dissertation, The University of Tulsa, 2000. 6. Ziegler, J. and Nichols, N.: "Optimum Settings for Automatic Controllers," ASME Transactions,

November 1942, pp. 759-767. ACKNOWLEDGMENTS The authors wish to thank Tulsa University Separation Technology Projects (TUSTP) member companies for supporting this project and U.S. Department of Energy (DOE) for the Grant: DE-FG22-97BC15024.

adaptive control technique a solution fortustp.org/publications/vasu isa03-p163.pdf · adaptive...

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