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Control of a Batch Polymerization System Using Hybrid Neural Network ‐ First Principle Model
Author(s) -
Wei Ng Cheah,
Hussain Mohamed Azlan,
Wahab Ahmad Khairi Abdul
Publication year - 2007
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450850616
Subject(s) - artificial neural network , internal model , control theory (sociology) , pid controller , inverse , computer science , process (computing) , controller (irrigation) , control (management) , hybrid neural network , set (abstract data type) , process control , control engineering , engineering , mathematics , artificial intelligence , temperature control , agronomy , geometry , biology , programming language , operating system
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.