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Article
Neural Network Control for a Batch Distillation Column

Authors: Duraid Fadhil Ahmed --- Ahlam Mohamad Shakoor
Journal: Tikrit Journal of Engineering Sciences مجلة تكريت للعلوم الهندسية ISSN: 1813162X 23127589 Year: 2016 Volume: 23 Issue: 1 Pages: 10-19
Publisher: Tikrit University جامعة تكريت

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Abstract

The present work deals with studying the dynamic behavior of a batch distillation column and implemented two types of control strategies for the separation different types of binary systems. The model was derived and then simulated using "MATLAB" program. The experimental data of dynamic behavior were to tune the parameters of PID controller and developed the training of neural networks controller by using supervised learning algorithms. The simulation results show a qualitatively acceptable behavior. This study shows also that the response of PID controller was oscillatory behavior with high offset value while neural network controller gave less offset value and less time to reach the steady state. In general, a good improvement is achieved when the neural network controller is used compared with PID control.


Article
The Dynamic Behavior and Control of Methanol-Toluene Distillation Column

Authors: Duraid F. Ahmed --- Maher O. Ahmed
Journal: Tikrit Journal of Engineering Sciences مجلة تكريت للعلوم الهندسية ISSN: 1813162X 23127589 Year: 2017 Volume: 24 Issue: 3 Pages: 1-11
Publisher: Tikrit University جامعة تكريت

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Abstract

In this study the dynamic behavior for two control methods of the distillation column for the separation of methanol and toluene mixture are studied. The experimental responses of temperature in each tray of distillation column for step changes in set point of reboiler, reflux ratio and feed weight fraction were obtained. Based on a derived mathematical model, the Simulink simulator of the distillation column is used to implement the PID and fuzzy logic control methods. The Comparison between two controllers is done for step changes in set point, feed flow rate, feed weight fraction and liquid reflux. The controller performance is measured by using mean square error and integral square error. The results showed that the performance of the fuzzy controller is the best than the PID controller in fast access to the desired value and cancelling the disturbances.

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