Research Article Open Access

Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor

R. Suja Mani Malar1 and T. Thyagarajan2
  • 1 , Afganistan
  • 2 ,
American Journal of Engineering and Applied Sciences
Volume 2 No. 1, 2009, 229-235

DOI: https://doi.org/10.3844/ajeassp.2009.229.235

Submitted On: 17 October 2008 Published On: 31 March 2009

How to Cite: Malar, R. S. M. & Thyagarajan, T. (2009). Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor . American Journal of Engineering and Applied Sciences, 2(1), 229-235. https://doi.org/10.3844/ajeassp.2009.229.235

Abstract

Continuous Stirred Tank Reactor (CSTR) is one of the common reactors in chemical plant. Problem statement: Developing a model incorporating the nonlinear dynamics of the system warrants lot of computation. An efficient control of the product concentration can be achieved only through accurate model. Approach: In this study, attempts were made to alleviate the above mentioned problem using "Artificial Intelligence" (AI) techniques. One of the AI techniques namely Artificial Neural Networks (ANN) was used to model the CSTR incorporating its non-linear characteristics. Two nonlinear models based control strategies namely internal model control and direct inverse control were designed using the neural networks and applied to the control of isothermal CSTR. Results: The simulation results for the above control schemes with set point tracking were presented. Conclusion: Results indicated that neural networks can learn accurate models and give good nonlinear control when model equations are not known.

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Keywords

  • Artificial neural networks
  • isothermal continuous stirred tank reactor
  • modeling
  • nonlinear system