Document Type

Article

Publication Date

1-1-2009

Abstract

The control of fluidized-bed processes remains an area of intensive research due to their complexity and the inherent nonlinearity and varying operational dynamics involved. There are a variety of problems in chemical engineering that can be formulated as Nonlinear Programming (NLP) problems. The quality of the solution developed significantly affects the performance of such a system. Controller design involves tuning of the process controllers and their implementation to achieve a specified performance of the controlled variables. Here we used a Sequential Quadratic Programming (SQP) method to tackle the constrained high-NLP problem, in this case a modified mathematical model of gas-phase olefin polymerisation in a fluidized-bed catalytic reactor. The objective of this work was to present a comparative study; PID control was compared to an advanced neural network-based MPC decentralised controller, and the effect of SQP on the performance of the controlled variables was studied. The two control approaches were evaluated for set-point tracking and load rejection properties, both giving acceptable results.

Keywords

Model predictive control, Neural networks, Optimisation, Proportion integral derivative control

Publication Title

Australian Journal of Basic and Applied Sciences

ISSN

1991-8178

Divisions

fac_eng

Volume

3

Issue

4

Publisher

American-Eurasian Network for Scientific Information, Jordan

Additional Information

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