Computational Analysis of Reactor Temperature Control of Batch Polymerization Process
DOI:
https://doi.org/10.71086/IAJSE/V12I4/IAJSE1288Keywords:
Autoregressive Exogenous ARX, Extended Kalman Filter-Nonlinear Model-Based Controller EKF Nmbc, Initiator Ammonium Persulphate Concentration I, Jacket Temperature Tj, Least Mean Square Lms, Monomer Concentration M, Nonlinear Autoregressive Exogenous NARX, Output Y, Reactor Temperature Tr, Unscented Kalman Filter-Nonlinear Model-Based Controller UKF NMBC.Abstract
Batch reactor is non-linear process plays major role in many chemical industries. Modeling and controlling of its parameters are very ambitious job. Because generalized modeling is not suitable all kinds of batch process, apart from general parameters, each batch has its own governing parameters. The common parameter plays major role in batch reactor are reactor temperature and coolant Jacket temperature. controlling reactor temperature helps in achieving desired final product. In this work polymerization batch reactor is taken for computational analysis and Autoregressive exogenous model is identified for reactor temperature profile by lease mean square estimation technique. The developed model is used as a reference model for nonlinear model-based controller, whose control parameters are estimated by Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF). Additionally, conventional cascade control is developed by traditional method and Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF) are deployed to identify PID controller gain values. The results of all the developed control strategies are analyzed by performance indices. the best fit control strategies are highlighted and recommended for future analysis.
