alexa Artificial Neural Networks Controller for Crude Oil Dis
ISSN: 2157-7048

Journal of Chemical Engineering & Process Technology
Open Access

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Research Article

Artificial Neural Networks Controller for Crude Oil Distillation Column of Baiji Refinery

Duraid Fadhil Ahmed* and Ali Hussein Khalaf
Department of Chemical Engineering, University of Tikrit, Iraq
Corresponding Author : Duraid Fadhil Ahmed
Department of Chemical Engineering
University of Tikrit, Iraq
E-mail: [email protected]
Received January 13, 2016; Accepted February 08, 2016; Published February 13, 2016
Citation: Ahmed DF, Khalaf AH (2016) Artificial Neural Networks Controller for Crude Oil Distillation Column of Baiji Refinery. J Chem Eng Process Technol 7:272. doi:10.4172/2157-7048.1000272
Copyright: © 2016 Ahmed DF, et al. 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.


A neural networks controller is developed and used to regulate the temperatures in a crude oil distillation unit. Two types of neural networks are used; neural networks predictive and nonlinear autoregressive moving average (NARMA-L2) controllers. The neural networks controller that is implemented in the neural network toolbox software uses a neural network model of a nonlinear plant to predict future plant performance. Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. A comparison has been made between two methods to test the effectiveness and performance of the responses. The results show that a good improvement is achieved when the NARMA-L2 controller is used with maximum mean square error of 103.1 while the MSE of neural predictive is 182.7 respectively. Also shown priority of neural networks NARMA-L2 controller which gives less offset value and the temperature response reach the steady state value in less time with lower over-shoot compared with neural networks predictive controller.


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