Two Case Studies for Applying Model Predictive Controllers on Chemical Processes

Transcription

1 The 33rd Annual Conference of the EEE ndustrial Electronics Society (ECON) Nov. 5-8, 27, Taipei, Taiwan Two Case Studies for Applying Model Predictive Controllers on Chemical Processes N. Danesh Pour, A. Montazeri, J. Poshtan, M.R. Jahed Motlahgh ran University of Science and Technology n daneshpour@iust. ac. ir, amontazeri@iust. ac.ir, jposhtangius. ac.ir, j ahedmr@iust.ac. ir Abstract- mplementation of Model Predictive Control as the most famous advanced process control method, in real processes has some practical issues that are ignored in many simulation studies and needs more attention especially in implementing the controller. For this purpose, in this paper two chemical processes are simulated in HYSYS software as a more realistic environment that exhibits many properties of real plants. The control and identification is performed by connecting HYSYS and MATLAB softwares in a real-time manner. Some of the main practical issues that are considered here are: pre-test of the plant, test design and identification requirements, dealing with low-level control loops and controller tuning to attain the best performance. Simulation results show that without having enough knowledge on such important points, practical implementation of this very useful advanced process control method will be very challenging.. NTRODUCTON Model Predictive Control (MPC) is widely adopted in the process industry as an effective means to deal with large multivariable constrained control problems. The main idea of MPC is to choose the control action by online repeated solving of an optimal control problem. This aims at minimizing a performance criterion over a future horizon, possibly subject to constraints on the manipulated inputs and outputs, where the future behavior is computed according to a model of the plant. MPC has been used in industry for more than 3 years, and has become an industry standard (mainly in the chemical and petrochemical industry) due to its intrinsic capability for dealing with constraints and with multivariable systems. Most commercially available MPC technologies are based on a linear model of the process [1]. n spite of this amount of practical implementations of MPC in process control in the last decades, a very few percents of thousands of papers published in this area are dedicated to study practical issues of applying model predictive control [2, 3]. n this work two different chemical processes are simulated in HYSYS software and a complete procedure for applying MPC control is done for each one including pre tests, design of test signal, system identification, controller design, pre tuning, final tuning and dealing with low-level control loops. The paper is organized in five sections. After this introduction, section 2 gives a review to MPC method and its corresponding system identification procedure used in this work. Section 3 is a brief overview on how to apply model predictive control on low-level loops and their relative /7/$2. C 27 EEE considerations. n section 4, two chemical processes are introduced and simulation results are shown. Final section gives some concluding remarks.. MODEL PREDCTVE CONTROL n this section a brief introduction to model predictive control and system identification methods used in this work is given. A. MPC Algorithm Various MPC techniques such as Dynamic Matrix Control (DMC) [4], Model Algorithmic Control (MAC) [5], and nternal Model Control (MC) [6] have demonstrated their effectiveness in industrial applications during the last two decades [1, 6, 7]. However, generalization of the 'traditional' MPC techniques to more complex cases has been with some difficulties. For example, most of the traditional techniques incorporate feedback into the algorithm in an ad hoc way, such as by adding a constant bias term in the prediction of the future outputs. n addition, the traditional techniques are not applicable to integrating systems (which are common in chemical process industries), when using step or impulse response models. Recently, there have been efforts to interpret MPC in state-space framework [8, 9, 1]. This not only permits the use of well-known state-space theorems, but also allows straightforward generalization to more complex cases such as systems with general stochastic disturbances and measurement noise. n this work, MPC formulation presented in [11] is adopted for controller design based on state-space model. B. System dentification Procedure The most important, time consuming and costly part in implementation of model predictive control is process identification [12]. This includes gathering priori information about the system, pre-tests, identification test design, data treatment and applying identification algorithm on collected data. Operator information and historical data can be very useful to prevent doing pre-tests and even some final tests. But when there is not any information available a priori (like the one in this work) pre-tests are necessary. The simplest way to do the pre-test is to apply step change to each manipulated input and measurable disturbance separately. This is required for determining proper sample time based on the fastest response of the process and also getting some idea about ranges of the semi-linear operation of the process. The proper sampling time will influence the quality of the identified model 58

2 and transient response of controlled process. Also, linear operation ranges of the process give an approximate magnitude of the final test signals. There are some useful test signals which are used in industrial MPC products such as multi-input step tests and PRBS signals. Here, a quasi-prbs signal with modified spectrum, named Generalized Binary Noise (GBN) [12] is used, which is especially fitted to MPC application and used more frequently in real applications. Low-pass filtering on collected data is needed, if there is measurement noise. After data preparation, a subspace identification algorithm, named SM [13], is used to identify a state space model of the process.. APPLYNG MPC ON Low-LEVEL CONTROL LooPs B. Casecadet Control Cascade control is depicted in Fig. 2. The following procedure applies to this approach: 1. dentify a process model in which the inputs are setpoints to the low-level loop controllers and the outputs are process variables. 2. Design a model predictive controller. The resulting cascade control structure helps to eliminate the disturbances that may occur inside the loops more efficiently and quickly. n addition, identification can be made easier since unstable or excessively slow dynamics are stabilized or made faster by regulatory loops. On the other hand, since the valves are not directly manipulated, handling of valve constraints becomes more complicated and also performance of the MPC controller is affected by the inner-loop controllers. Traditionally industrial processes have relied strictly on regulatory loops that are designed to stabilize operation of the process. These loops are generally based on regulatory sp control. When implementing an advanced control system, such as Model Predictive Control, on a process with regulatory loops, one is faced with the following two options [14]: Fig... Cascade MPC implementation Direct Control: Break the loops and have the model based controller to manipulate the control valves directly. Fig. 2. Cascade MPC implementation Cascade Control: Leave the loops and have the model based controller to manipulate the setpoints of these loops. n the following a brief overview on how to interfacing V. SMULATONS model predictive control and low-level control loops is n this section, at first two chemical processes and the presented which is mainly based on [14]. reasons for selecting them as the benchmark for this study are A. Direct Control introduced. Also, results of identification and control gained by A general scheme of direct control is represented in Fig. 1. HYSYS and MATLAB softwares in a real-time connecting n this appraoch, the following procedure must be followed: environment will be presented. The models of these processes 1- Break the low-level loops. are selected from [15]. 2- dentify a process model. nputs are the valve positions A. Plug-flow reactor and outputs are process variables. The first process is a plug-flow reactor (PFR) which is useful 3-Design a conventional model predictive control. Since the inputs of the model are the valve positions, valve in many industrial processes particularly those in which a solid limits can be entered directly into the algorithm as the input catalyst is required. This reactor has a vessel that is packed constraints. Therefore, handling of the actuator constraints can with solid catalyst. Unlike CSTR reactors, in tubular reactors be done straightforwardly. However, because the low-level temperature and composition vary down the length of the loops are taken out, one may lose the efficiency in disturbance reactor, and they also vary with time. This makes models and rejection. Note that disturbance dl must propagate through the dynamics more complicated. A diagram of this process is shown in Fig. 3 in a HYSYS folwsheet. Outlet pressure is process to affect y before any control action can take place. controlled by valve V6 and reactor temperature is controlled by coolant water flow rate. Feed flowrate is considered as a d2l dil sp disturbance variable. y 2* _ NWC ~~~~Plant PFR3 5="""<; PC3..R3ut VS5 F3 -. Q3 Fig. 1. Direct MPC implementation Furthermore, identification can be unsafe, costly and timeconsuming with these loops taken out, especially when the process contains unstable or excessively slow dynamics. 581 FC3 TC3 v5out lags3 Fig. 3. Plug-flow reactor flowsheet * ~~~~~~~~~~~v6out ~~~~~~~V6

3 n order to obtain an idea on how to manage control loops to get the performance simulations are performed in four different control structures: Only control, direct MPC, cascade MPC and control and finally one loop controlled by and another one by direct MPC. n the first, second and fourth structures, control is run by embedded controllers in HYSYS software and in all cases, MPC is run in MATLAB environment. For each structure, both disturbance rejection and setpoint tracking capabilities of the controllers are examined. n each case, best controller tunings are obtained for regulatory response, because the most common task defined for a real process is to work on a predefined operating point and the most important task of the controller is rejecting the effects of arriving disturbances. A complete system identification procedure is done individually for each of the mentioned MPC implementation approaches and three different state space models are identified. The step test results depicted in Fig. 4 are used to determine the sampling time (Ts), magnitude of the GBN signals and duration of test signal to excite the plant around its nominal operating point. Based on the shortest settling time of different channels with and without loops, sampling time to collect data is chosen to be 3 seconds for all identification experiments. Besides, the magnitude of the GBN test signal has been set to 5 percents of nominal value for each input. The duration of the test by the measure proposed in [12] is chosen 18 minutes. The first half of data is selected for identification and the second half is used for validation purpose. temperature the system are plotted in log-scale in Fig. 7. Based on this initial result and some trial and errors, the order ofthe model is selected n=12. The identified state-space matrices are validated using second half of data as shown in Fig. 8. The VAF criteria for this data are for output 1 and for output 2. Besides, the step responses of the real plant and identified model for two outputs are plotted in Fig. 9. This step response exhibits a better validation for MPC framework. The identification for two other structures is accomplished in the same way and due to lack of space their results are not included here. These models are used for predicting the behavior ofthe process in model predictive controller. The control results for applying four different structures are shown in Figs. 1 to 13. controllers are tuned based on relay-feedback method [16]. These controllers have fairly good disturbance rejection, but poor setpoint tracking. However, as can be seen in Fig. 1, the response of temperature controller to input flow change assumed as a disturbance, is slow. Direct MPC and Cascade MPC- implementations show quite different responses, especially in the tracking problem. pressure /2 \ TC setpoint 3. 28hll PC setpoint ~ Feed rate /.42, 177 U. 1+1_ Fig. 5. GNB test signals temperature C Pnn,~~~~~~~~~~~~~~~~~~~~ 27k pressure Fig. 4. Step pre-test results 19 By these settings, the GBN test signals which excite the plant around the nominal inputs are shown in Fig 5. Since the aim is to obtain a single multivariable state-space model, these inputs are applied simultaneously and the resulting outputs are shown in Fig. 6. Before identification, in order to obtain a measure of the order of the system, Hankel singular values of u 2 ~~~ i Fig. 6. GNB test signals

4 - a) 1 'in 1 1 singular value plot io number Fig. 7. singular values ofthe plant using collected data 2 X real output L validation output TT 1 T T~~ 3 performance. As can be seen in Figs. 11 and 12 regulatory responses are different but both are better than case. However, pressure regulation is better handled by the control. This is because of the interaction effects between inputs in a multivariable structure. Direct MPC shows faster tracking with overshoots and no offset error in comparison with cascade MPC- control which presents a slow tracking with offset error and no overshoots. Properties of the latter approach mainly come from control system behaviour. n comparison with control it can be said that an overall better performance is obtained, but referring to pressure changes and its corresponding control actions, these two structures are not completely suitable for use in this application. Another control structure is obtained by breaking temperature control loop and using MPC to do this job while keeping control for pressure regulation. This approach gives the best results in comparison with three previous methods. Fast tracking, better pressure and temperature regulations and completely acceptable control actions are obtained in this way F xl C\M t(sec) x 14 3 Fig. 8. Plant and model outputs for validation data Step response of identified low order model vs. real plant Direct MPC Fig Temperature control (tracking response) lyu:', )OO t(sec) Step responses of plant and identified model Fig. 9. dentified linear models were used for the initial parameter tuning of MPC controllers in each case and some corrections in the tuned parameter is required for obtaining the best possible B. Azeotropic Distillation Column Distillation is the most frequently used separation technique in the chemical and petroleum industries. The design and control of this important unit operation is vital for the safe and profitable operation of many plants. The column used here for simulations is a heterogeneous azeotropic distillation column. n theoretical columns ideal VLE behaviour is assumed, but in this column components have such positive deviations from ideality (large repulsion) that two liquid phases can occur. The column operates at kpa at the top and 152 kpa at the bottom. The decanter temperature is 4 degree C and condenser duty is designed to be 16 GJ/hr. 583

5 Vale V6 action Cascade MPC- MPC& 273 -~~~~~~~ _ 173 o Fig. 11. Temperature control (regulatory respor Direct MPC Fig. 12. Direct MPC Direct MPC Cascade MPC- MPC& l / Casca Direct Casca MPC& Pressure control (regulatory response Q3 utility flow rate 4 _ Fig. 13. Control actions nse) Column pressure is controlled by valve V6 in the overhead vapour line using PC. Decanter temperature is controlled by manipulating decanter heat removal (TC3) and a tray temperature (tray 5) is controlled by manipulating reboiler heat de MPC- input (TC1). n addition to these controlled variables, there are three associated variables which must be maintained in a 'D specific range: column base level, aqueous and organic levels. Column feed rate and refluxed organic phase flow rate are - considered as measurable disturbances. The nominal value of these two DVs are 275 and 35 kmol/hr, respectively. Since the task of keeping the mentioned levels in required limits can be done well by controllers, the best approach here is to maintain these loops unchanged and cascade \PC control with low-level loops in column pressure and MPC temperature and decanter temperature control. The main control problem is to maintain controlled variables 5 6 in their setpoints against incoming disturbances. For evaluation of setpoint tracking ability of the controller, a step change in decanter temperature is also considered to be followed by controller. Simulation is carried out for 6 hours. After one 1~~~- Amm- r RCYt-1 L FC2 Fig. 14. UsU-Ufl v wk3wk cut Azeotropic distillation column flowsheet W1k _tx' LG p 1 pl V22out,C 584

6 hour, column feed rate has been changed to 335 kmol/hr and at selected for fast control loops while direct MPC is suitable for minute 15, a step reduction in refluxed organic flow rate h as slower loops. Anyway, our results show that part of control been applied. After 4 hours, decanter temperature setpoint h as task should be performed by previous structure without been changed to 35 degree oc. Figs. 15 and 16 shows tl,he any change. results of simulations for comparing control performan ice The most difficult part of MPC implementation is model with cascade MPC- structure in both disturbance rejecti on identification. n cases that historical data or operators and setpoint tracking. experience is not available, a very risky and time consuming procedure is also needed to be done for gathering information of the plant linear operating range, suitable sampling time and V. CONCLUSON duration of the test signal. Here multivariable state-space n this paper, the task of applying model predictive contrrol models were identified for all cases studied and the results on two chemical processes was considered. HYSYS arnd were validated using step response data and validation outputs M\ATLAB were connected in the same computer to provide of the real plant. Validation is one of the important parts in real-time environment for simulations. HYSYS software w(,as identification procedure, because if the identified model gives used for simulating dynamic behavior of the processes ai nd poor predictions, plant retesting will be required that is very M\ATLAB software was connected to it in order to run contrrol costly and undesirable for plant owners. The results obtained algorithms. Different control strategies were examined on ea(ch from this study show why the use of dynamic simulators for process and simulation results showed that in these two case evaluating the performance of advanced controllers before the best approach for improving control performance is to ke(ep practical implementations is so popular today. some control loops under control and replace the others tby or cascade them with model predictive control systerm. REFERENCES However, selection of a suitable structure is not necessariily [1] S.J. Qin and T.A. Badgwell, "A survey of industrial model predictive easy. n [14], it is suggested that cascade control might b( e Column temperature (tray 5) 121 [2] [3] [4] [5] [6] [7] Decanter temperature 1, step change to feed rate [8] [9] step change to refluxed organic flow rate 36 [1] l, [11] Fig. 15. Column and decanter temperatures [12] [13] Column pressure A _--12,~-- V /- [14] 1 [15] % ' 123 _ MPC [16] 122 H Fig. 16. Column pressure 585 control technology," Control Engineering Practice, vol. 11, pp , 23. D. Morrison. "s it time to replace?," ntech Letters, March 25. Morari, M. and JH Lee "Model Predictive Control: Past, Present and Future,"Computer and Chemical Engineering," vol. 23, pp , C.R. Cutler, and B.L. Remaker, "Dynamic matrix control a computer control algorithm," in Proc Automatic Control Conf, Paper WP5-B, San Francisco, CA, 198. R. Rouhani, and R.K. Mehra, "Model algorithmic control (MAC): basic theoretical properties," Automaitca, 18, 41-46, C.E. Garcia, D.M. Prett, and M. Morari, "Model predictive control theory and practice-a survey," Automatica, 25, , J. Richalet, A. Rault, J.L. Testud, and J. Papon, "Model predictive heuristic control application to industrial processes," Automatica, Vol. 14, pp , K.Y. Lim, and D.G. Fisher, "A state space formulation for model predictive control," AChE Journal, Vol. 35, pp , J.P. Navratil, K.Y. Lira, and D.G. Fisher, "Disturbance feedback in model predictive control systems," Proc FAC Workshop on ModelBased Process Control, pp , Atlanta, GA, J.H. Lee, M. Morari, and C.E. Garcia, "State-space nterpretation of Model Predictive Control," Automatica, Vol. 3, No. 4, pp , M. Maciejowski, Predictive Control With Constraints. Prentice-Hall, London, 22. Y. Zhou, Multivariable System dentification For Process Control. Elsevier, 21. M. Verhaegen, P. Dewilde "Subspace model identification: part 1: The output-error state-space model identification class of algorithms" nt. J. Control, Vol. 56, No.1, pp , Y. Lee, J.H. Lee and S. Park, "On interfacing model predictive controllers with low-level loops," ndustrial and Engineering Chemistry Research, Oct W.L. Luyben, Plantwide Dynamic Simulators n Chemical Processing and control. M.Dekker, 22. K Astrom, and T. Haggland, Controllers: Theory, Design and Tuning. Second ed., SA, 1995.