Application of Predictive Control to a Batch Reactor (original) (raw)
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Generalized Predictive Control Of Batch Polymerization Reactor
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This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking referen...
ACS Omega
Batch process plays a very crucial and important role in process industries. The increased operational flexibility and trend toward high-quality, low-volume chemical production has put more emphasis on batch processing. In this work, nonlinearities associated with the batch reactor process have been studied. ARX and NARX models have been identified using open-loop data obtained from the pilot plant batch reactor. The performance of the batch reactor with conventional linear controllers results in aggressive manipulated variable action and larger energy consumption due to its inherent nonlinearity. This issue has been addressed in the proposed work by identifying the nonlinear model and designing a nonlinear model predictive controller for a pilot plant batch reactor. The implementation of the proposed method has resulted in smooth response of the manipulated variable as well as reactor temperature on both simulation and real-time experimentation.
Model predictive control of a PVC batch reactor
Computers & Chemical Engineering, 1997
This paper discusses a method to improve the quality of temperature control of the polyvinyl chloride (PVC) suspension batch reactor by implementing two Nonlinear Model Predictive Controllers (NMPCs) for this exponentially unstable process with very nonlinear behavior. The first method is based on recomputing the step response matrix at each sampling time with a double model based prediction and solving twice the optimization problem using the linear model of the process described by the step response matrix. The second method uses the rigorous model for both prediction and optimization. These methods were tested for different disturbances, and their performances were compared with those obtained with proportional-integral-derivative (PID) control. Significant improvement of temperature control was achieved using NMPC. The process model, used in both NMPC methods, is presented.
APPLICATION OF MODEL PREDICTIVE CONTROL TO BATCH POLYMERIZATION REACTOR
The absence of a stable operational state in polymerization reactors that operates in batches is factor that determine the need of a special control system. In this study, advanced control methodology is implemented for controlling the operation of a batch polymerization reactor for polystyrene production utilizing model predictive control. By utilizing a model of the polymerization process, the necessary operational conditions were determined for producing the polymer within the desired characteristics. The main control objective is to bring the reactor temperature to its target temperature as rapidly as possible with minimal temperature overshoot. Control performance for the proposed method is encouraging. It has been observed that temperature overshoot can be minimized by the proposed method with the use of both reactor and jacket energy balance for reactor temperature control.
A neural network model based predictive control approach: application to a semi-batch reactor
Neural networks can be considered to be new modelling tools in process control and especially in non-linear dynamical systems cases. Their ability to approximate non-linear functions has been very often demonstrated and tested by simulation and experimental studies. In this paper, a predictive control strategy of a semi-batch reactor based on neural network models is proposed. Results of a non-linear control of the reactant temperature of a semi-batch reactor are presented. The process identification is composed of an off-line phase that consists in training the network, and of an on-line phase that corresponds to the neural model adaptation so that it fits any modification of the process dynamics. Experimental results when using this method to control a semi-batch reactor are reported and show the great potential of this strategy in controlling non-linear processes.
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This paper investigates experimentally the application of a nonlinear model based predictive control (NLMBPC) to a class of semi-batch chemical reactors equipped with a mono-fluid heating/cooling system. We present the experimental results dealing with a strongly exothermic reaction carried out in a small pilot plant according to a procedure commonly used in industry. The application of the NLMBPC is based on a constrained optimisation problem solved repeatedly on-line. The control objective is to keep the reactor temperature within safe operating specifications by manipulating the heating power. Experimental results demonstrate that this control strategy works well in the presence of hard constraints and load disturbances.
Model-based control strategies for a chemical batch reactor with exothermic reactions
Korean Journal of Chemical Engineering
Batch reactor control provides a very challenging problem for the process control engineer. This is because a characteristic of its dynamic behavior shows a high nonlinearity. Since applicability of the batch reactor is quite limited to the effectiveness of an applied control strategy, the use of advanced control techniques is often beneficial. This work presents the implementation and comparison of two advanced nonlinear control strategies, model predictive control (MPC) and generic model control (GMC), for controlling the temperature of a batch reactor involving a complex exothermic reaction scheme. An extended Kalman filter is incorporated in both controllers as an on-line estimator. Simulation studies demonstrate that the performance of the MPC is slightly better than that of the GMC control in nominal case. For model mismatch cases, the MPC still gives better control performance than the GMC does in the presence of plan/model mismatch in reaction rate and heat transfer coefficient.