TITLE:
Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery
AUTHORS:
Lekan T. Popoola, Alfred A. Susu
KEYWORDS:
Neuron, Monte Carlo Simulation, Crude Oil Distillation Column, Artificial Neural Networks, Architecture, Refinery, Design, Control
JOURNAL NAME:
Advances in Chemical Engineering and Science,
Vol.4 No.2,
April
24,
2014
ABSTRACT:
This research work investigated comparative studies of expert system
design and control of crude oil distillation column (CODC) using artificial
neural networks based Monte Carlo (ANNBMC) simulation of random processes and
artificial neural networks (ANN) model which were validated using experimental
data obtained from functioning crude oil distillation column of Port-Harcourt
Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the
experimental data sets were used for training while ten percent (10%) were used
for testing the networks. The maximum relative errors between the experimental
and calculated data obtained from the output variables of the neural network
for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC
were used respectively while their respective values for the maximum relative
error were 0.346 error % and 0.124 error % when they were used for the
controller prediction. Larger number of iteration steps of below 2500 and 5000
were required to achieve convergence of less than 10-7for the
training error using ANNBMC for both the design of the CODC and controller
respectively while less than 400 and 700 iteration steps were needed to achieve
convergence of 10-4using ANN
only. The linear regression analysis performed revealed the minimum and maximum
prediction accuracies to be 80.65% and 98.79%; and 98.38% and 99.98% when ANN
and ANNBMC were used for the CODC design respectively. Also, the minimum and
maximum prediction accuracies were 92.83% and 99.34%; and 98.89% and 99.71% when
ANN and ANNBMC were used for the CODC controller respectively as both
methodologies have excellent predictions. Hence, artificial neural networks
based Monte Carlo simulation is an effective and better tool for the design and
control of crude oil distillation column.