Open AccessDevelopment of Artificial Neural Network Model of Crude Oil Distillation Column
Author(s) -
Duraid Fadhil Ahmed,
and Ali Hussein Khalaf
Publication year - 2015
Publication title -
mağallaẗ tikrīt li-l-ʻulūm al-handasiyyaẗ/tikrit journal of engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 2312-7589
pISSN - 1813-162X
DOI - 10.25130/tjes.22.1.03
Subject(s) - artificial neural network , nonlinear autoregressive exogenous model , autoregressive model , distillation , computer science , matlab , fractionating column , backpropagation , set (abstract data type) , artificial intelligence , machine learning , statistics , mathematics , chemistry , organic chemistry , programming language , operating system
Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARX) and back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining thirty percent are used for testing and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.