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.
Original language | English |
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Pages (from-to) | 3954-3974 |
Number of pages | 21 |
Journal | Australian Journal of Basic and Applied Sciences |
Volume | 3 |
Issue number | 4 |
Publication status | Published - Jan 1 2009 |
Keywords
- Model predictive control
- Neural networks
- Optimisation
- Proportion integral derivative control
ASJC Scopus subject areas
- General