Performance Analysis of Two Advanced Controllers for Polystyrene Polymerization in Batch Reactor

The performance of two advanced model based non-linear controllers is analyzed for the optimal setpoint tracking of free radical polymerization of styrene in batch reactors. Artificial neural network-based model predictive controller (NN-MPC) and generic model controller (GMC) are both applied for controlling the system. The recently developed hybrid model [1] as well as available literature models are utilized in the control study. The optimal minimum temperature profiles are determined based on Hamiltonian maximum principle. Different types of disturbances are artificially generated to examine the stability and robustness of the controllers. The experimental studies reveal that the performance of NN-MPC is superior over that of GMC.

[1]  Dan Sui,et al.  Explicit moving horizon control and estimation: A batch polymerization case study , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[2]  H. Hapoglu,et al.  Application of experimental non-linear control based on generic algorithm to a polymerization reactor , 2009 .

[3]  Z. Zeybek,et al.  Adaptive heuristic temperature control of a batch polymerisation reactor , 2004 .

[4]  Hale Hapoglu,et al.  Control of a polymerization reactor by fuzzy control method with genetic algorithm , 2003, Comput. Chem. Eng..

[5]  Francesco Pierri,et al.  Control and Monitoring of Chemical Batch Reactors , 2011 .

[6]  J. Rawlings,et al.  Model-predictive control of chemical processes , 1992 .

[7]  John W. Eaton,et al.  Model Predictive Control of Chemical Processes , 1991, 1991 American Control Conference.

[8]  Jie Zhang,et al.  Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models , 2008 .

[9]  Hale Hapoglu,et al.  Self-tuning PID control of jacketed batch polystyrene reactor using genetic algorithm , 2008 .

[10]  Iqbal M. Mujtaba,et al.  Evaluation of neural networks-based controllers in batch polymerisation of methyl methacrylate , 2008, Neurocomputing.

[11]  A. Jalali,et al.  Nonlinear predictive control of a polymerization reactor based on piecewise linear Wiener model , 2008 .

[12]  Mohd Azlan Hussain,et al.  Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation , 2011 .

[13]  H. Sadoghi Yazdi,et al.  Fuzzy Temperature Control in a Batch Polymerization Reactor Using ANFIS Method , 2009 .

[14]  F. Mjalli,et al.  Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach , 2011 .

[15]  Doug Cooper,et al.  A Practical Multiple Model Adaptive Strategy for Multivariable Model Predictive Control , 2003 .