Control of a batch polymerization system using hybrid neural network first principle model
Document Type
Article
Publication Date
1-1-2007
Abstract
In this work, the utilization of neural network in hybrid with first principle models for modelling and control of a batch polymerization process was investigated. Following the steps of the methodology, hybrid neural network (HNN) forward models and HNN inverse model of the process were first developed and then the performance of the model in direct inverse control strategy and internal model control (IMC) strategy was investigated. For comparison purposes, the performance of conventional neural network and PID controller in control was compared with the proposed HNN. The results show that HNN is able to control perfectly for both set points tracking and disturbance rejection studies.
Keywords
Batch polymerization, First principle model, Hybrid neural networks, Model-based control, Modelling, Control equipment, Mathematical models, Neural networks, Three term control systems, Internal model control (IMC), Polymerization.
Divisions
fac_eng
Publication Title
Canadian Journal of Chemical Engineering
Volume
85
Issue
6
Publisher
Canadian Journal of Chemical Engineering
Additional Information
253IU Times Cited:0 Cited References Count:24