Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR)
In this paper a dynamic behavior and control of a jacketed continuous stirred tank reactor (CSTR) is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described...
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| Formato: | article |
| Lenguaje: | EN |
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Al-Khwarizmi College of Engineering – University of Baghdad
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6ac388c963d64b1fbad7347d72502aa6 |
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| Sumario: | In this paper a dynamic behavior and control of a jacketed continuous stirred tank reactor (CSTR) is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time.
The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller.
The results show that the artificial neural network is the best method to control the CSTR process and it is better than the conventional method because it has smaller value of mean square error (MSE). MATLAB program is used as a tool of solution for all cases used in the present work.
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