Performance Analysis of Three Controllers for the Polymerisation of Styrene in a Batch Reactor

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
Emmanuel E Ekpo ◽  
Iqbal M Mujtaba
Keyword(s):  
Performance Analysis ◽  
Batch Reactor ◽  
Monomer Conversion ◽  
Average Chain Length ◽  
Generic Model ◽  
Batch Reactors ◽  
Dynamic Optimisation ◽  
Model Control ◽  
Simple Kinetic Model ◽  
Generic Model Control

The performance analysis of three advanced non linear controllers is the main focus of this paper. All three controllers are applied for the control of a batch polymerisation reactor which is defined by a very simple kinetic model for the polymerisation of styrene. This simple set of equations describing the polymerisation process is first solved using the sequential strategy i.e. Control Vector Parameterisation (CVP) technique within gPROMS to find optimal initial initiator concentrations and the reactor temperature trajectory necessary to yield desired polymer molecular properties (defined here as fixed values of monomer conversion and number average chain length) in minimum time. The sequential solution strategy has had limited application in solving optimisation problems for polymerisation in batch reactors, most researchers instead employing the Pontryagin's Maximum Principle (PMP) to solve optimal control problems involving these systems.The temperature trajectory obtained from the dynamic optimisation is used as the setpoint to be tracked by the three controllers: Dual Mode control with PID, which is representative of industrial practice, Generic Model Control (GMC) with Neural Networks as online heat release estimator, and Direct Inverse Control (DIC). Published work on the last two controllers as applied to the control of a batch polymerisation reactor is absent from the literature.When the performances of the different controllers are evaluated, it is seen that the GMC-NN controller performs better than the other two for the system under consideration.

Application of Generic Model Control to Batch Reactor Temperature Control

2005 ◽  
Vol 31 (6) ◽  
pp. 435-440 ◽  
Author(s):  
Katsuyuki Suzuki ◽  
Katsumi Hanashima ◽  
Toshiyuki Enoki ◽  
Takamasa Kuramoto
Keyword(s):  
Temperature Control ◽  
Batch Reactor ◽  
Generic Model ◽  
Model Control ◽  
Reactor Temperature ◽  
Generic Model Control

Realization of Non Linear Controllers in Batch Reactor using GA and SVM

2011 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Sujatha ◽  
N. Pappa
Keyword(s):  
Batch Reactor ◽  
Waste Product ◽  
Internal Model Control ◽  
Linear Control ◽  
Generic Model ◽  
Model Control ◽  
Control Schemes ◽  
Non Linear ◽  
Generic Model Control ◽  
Almost All

This paper presents the application of machine learning schemes, namely SVM and GA, for realization of non linear control schemes and optimization of Batch reactor. Batch reactor is an essential unit operation in almost all batch- processing industries such as chemical and pharmaceuticals. In this approach, the temperature profile of the batch reactor is optimized using Genetic Algorithm (GA) with a view to maximize the desired product and minimize the waste product as a multi -objective function. Generic Model Control is implemented by using SVM Estimator, and it includes the non-linear model of a process to determine the control action. SVM estimator will predict the current value of the heat release makes the control performance to be more robust. The robustness performance of GMC has been experienced. Other non linear control schemes, such as Direct Inverse Control and Internal Model Control, are also implemented.

Optimal Control Trajectories for a Batch Polymerisation Reactor

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Emmanuel E Ekpo ◽  
Iqbal M Mujtaba
Keyword(s):  
Process Model ◽  
Batch Reactor ◽  
Average Molecular Weight ◽  
Monomer Conversion ◽  
Minimum Time ◽  
Average Chain Length ◽  
Dynamic Optimisation ◽  
Similar Product ◽  
Batch Time ◽  
Reactor Operating

This paper focuses on the determination of optimum control trajectories for the operation of a batch polymerisation reactor. Operating policies needed to achieve pre-specified properties in minimum time are obtained. Two models dealing with the polymerisation of styrene using AIBN as initiator in a batch reactor are considered. The first model (Model I), taken from the literature, is derived from the first principles of the polymerisation system while the other (Model II) is more rigorous involving both mass and energy balances to simulate a batch polymerisation reactor operation.A dynamic optimisation problem incorporating Model I is formulated and solved to find the optimal temperature trajectory and initial initiator concentrations that will yield desired levels of monomer conversion and number average molecular weight in minimum time. The total batch time is divided into a finite number of intervals and piecewise constant reactor temperature is assumed in each interval. The reaction temperature and the length of each interval are optimised using Control Vector Parameterisation technique. Significant savings in batch time are obtained when these results are compared with those available in the literature obtained using the Pontryagin's Maximum Principle.Incorporating Model II, a second dynamic optimisation problem is formulated and solved where the coolant flow rates and lengths of the intervals are optimised to minimise the batch time and yield desired levels of monomer conversion and number average chain length. With examples, the results show that although both optimisation problems result in similar product qualities, the optimal operations are significantly different. Batch time is significantly higher for the second optimisation problem. gPROMS modelling software was used to build the process model and perform the dynamic optimisation.

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