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.
 

Abstract

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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