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.
Highlights
The Continuous Stirred Tank Reactor system (CSTR) is a complex nonlinear system
This study describes the modeling and control of a isothermal CSTR using neural networks
Internal Model Control (IMC) scheme is one of the control strategies emanating from model based control schemes
Summary
The Continuous Stirred Tank Reactor system (CSTR) is a complex nonlinear system. Due to its strong nonlinear behavior, the problem of identification and control of CSTR is always a challenging task for control systems engineer. Neural networks are relatively less sensitive to noise and incomplete information and deal with higher levels of uncertainty when applied in process control problems[1].The multilayer feed forward neural networks offer interesting possibilities for modeling any nonlinear process without a priori knowledge[2]. Many variation of the basic algorithm that improves its performance have been suggested by Bhat[4].The use of momentum term generally speeds up the convergence and smoothes the trajectory of the weights during the update procedure. During training both learning rate and momentum can be modified in order to improve convergence.
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