Adaptive—predictive temperature control of semi-batch reactors (original) (raw)
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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.
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The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing con- centration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying pa- rameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time- variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separa...
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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...
Application of Nonlinear State Estimation to Batch Polymerization Reactors
IFAC Proceedings Volumes, 1998
Two estimation methods. namely the Extended Kalman Filter and the Receding Horizon State Estimator, were applied to a simulated batch solution polymerization reactor model. Parameter adaptive filters were found to be extremely successful in tracking timevarying model parameters (e.g., reactor fouling and time-varying kinetic rate constants). Finally, the problem of estimating the initial process states was considered, and two estimation algorithms (e.g., the reiterative Kalman Filter and a non-linear optimization based estimator) were implemented to track the initial initiator concentration and the initial value of the overall heat-transfer coefficient in a batch polymerization reactor.
Experimental nonlinear model based predictive control for a class of semi-batch chemical reactors
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.
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Generic model control (GMC) has been successfully used for achieving tight control of batch/semi-batch processes. As the requirement to developing a mechanistic model can prove to be a bottleneck while implementing GMC, many researchers have recently proposed GMC formulations based on black box models developed using artificial neural networks (ANN). The applicability of most of these formulations is limited to continuously operated systems with relative degree one. In addition, these formulations cannot handle constraints on inputs systematically. In the present study, ANN based GMC (ANNGMC) approach is extended to semi-batch processes with relative order higher than one. The nonlinear time-varying behaviour of batch/semi-batch processes is approximated using ANN model developed in the desired operating region. The ANN model is further used to formulate a nonlinear controller using GMC framework for solving trajectory-tracking problems associated with semi-batch reactors. The control problem at each sampling instant is formulated as a constrained optimization problem whereby the constraints on manipulated inputs can be handled systematically. The proposed controller formulation is used for solving trajectory-tracking problems associated with semi-batch reactors. The performance of the proposed control algorithm is evaluated by simulating the challenge problem proposed by Chylla and Haase (1993), which involves temperature control of a multi-product semi-batch polymerization reactor under widely varying operating conditions. The simulation exercise reveals that the performance of proposed ANNGMC formulation is comparable to the performance of the GMC formulation based on the exact mechanistic model, and is much better than PID controller performance.