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Neuro-estimator based GMC control of a batch reactive distillation
K J Jithin Prakash1, Dipesh S Patle, Amiya K Jana
1Department of Chemical Engineering, Indian Institute of Technology, Kharagpur, West Bengal 721 302, India.
ISA Transactions
|February 22, 2011
Summary
This study introduces an artificial neural network (ANN)-based control algorithm for batch reactive distillation (RD) columns producing ethyl acetate. The novel neuro-estimator based generic model controller (GMC) demonstrates improved performance over traditional methods.
Area of Science:
- Chemical Engineering
- Process Control
- Artificial Intelligence
Background:
- Batch reactive distillation (RD) is crucial for producing chemicals like ethyl acetate.
- Controlling these complex processes requires advanced algorithms to manage dynamics.
- Existing control methods may not fully capture the nonlinear behavior of RD systems.
Purpose of the Study:
- To develop and evaluate a novel artificial neural network (ANN)-based nonlinear control algorithm for a simulated batch reactive distillation column.
- To address the control challenges in homogeneously catalyzed esterification processes, specifically for ethyl acetate production.
- To compare the performance of the proposed controller against a gain-scheduled proportional integral (GSPI) controller and an ideal generic model controller (GMC).
Main Methods:
- A fundamental model for the batch reactive distillation column was derived, incorporating the esterification reaction kinetics.
- An artificial neural network (ANN)-based state predictor was developed.
- A neuro-estimator based generic model controller (GMC) was synthesized, combining the ANN predictor with a GMC law.
- The control algorithm was simulated and tested on a representative batch reactive distillation process.
Main Results:
- The ANN-based nonlinear control algorithm was successfully implemented on a simulated batch reactive distillation column.
- The proposed neuro-estimator based GMC demonstrated effective control over the process dynamics.
- Performance comparisons indicated advantages of the proposed control strategy over a GSPI controller.
Conclusions:
- The proposed artificial neural network (ANN)-based nonlinear control algorithm offers a promising approach for enhancing the control of batch reactive distillation processes.
- The neuro-estimator based generic model controller (GMC) provides a robust and effective solution for complex chemical production systems.
- This research contributes to the advancement of intelligent control strategies in chemical engineering applications.
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