American Journal of Engineering and Applied Sciences

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

R. Suja Mani Malar and T. Thyagarajan

DOI : 10.3844/ajeassp.2009.229.235

American Journal of Engineering and Applied Sciences

Volume 2, Issue 1

Pages 229-235


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 &#34Artificial Intelligence&#34 (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.


© 2009 R. Suja Mani Malar and T. Thyagarajan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.