Advanced control of a fluidized bed using a model-predictive controller
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 qu...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American-Eurasian Network for Scientific Information, Jordan
2009
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/7036/1/Advanced_control_of_a_fluidized_bed_using_a_model-predictive_controller.pdf http://eprints.um.edu.my/7036/ http://www.ajbasweb.com/old/ajbas/2009/3954-3974.pdf |
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| Summary: | 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. |
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