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

Affiliation(s)

Department of Chemical and Petroleum Engineering, Afe Babalola University, Ado-Ekiti, Nigeria.

Department of Chemical Engineering, University of Lagos, Akoka, Nigeria.

Department of Chemical and Petroleum Engineering, Afe Babalola University, Ado-Ekiti, Nigeria.

Department of Chemical Engineering, University of Lagos, Akoka, Nigeria.

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^{-7} for 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^{-4} using 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.

KEYWORDS

Neuron, Monte Carlo Simulation, Crude Oil Distillation Column, Artificial Neural Networks, Architecture, Refinery, Design, Control

Neuron, Monte Carlo Simulation, Crude Oil Distillation Column, Artificial Neural Networks, Architecture, Refinery, Design, Control

Cite this paper

Popoola, L. and Susu, A. (2014) 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.*Advances in Chemical Engineering and Science*, **4**, 266-283. doi: 10.4236/aces.2014.42030.

Popoola, L. and Susu, A. (2014) 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.

References

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http://dx.doi.org/10.1016/0893-6080(89)90020-8

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http://dx.doi.org/10.4236/aces.2013.32020

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http://dx.doi.org/10.1109/34.58871

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http://dx.doi.org/10.5194/hess-7-680-2003

[13] Kuo, R.J., Liao, J.L. and Tu, C. (2005) Integration of ART2 Neural Network and Genetic K-means Algorithm for Analyzing Web Browsing Paths in Electronic Commerce. Decision Support Systems, 40, 355-374.

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[14] Jia, Y.X. and Guo, X.Y. (2006) Monte Carlo Simulation of Methanol Diffusion in Critical Media. Chinese Journal of Chemical Engineering, 14, 413-418.

http://dx.doi.org/10.1016/S1004-9541(06)60093-1

[15] Yeh, W.C., Yu, C.Y. and Lin, C.H. (2007) Evaluate Voting System Reliability using the Monte Carlo Simulation and Artificial Neural Network. 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, Sydney, 27-30 August 2007, 45-49. http://dx.doi.org/10.1109/AUSWIRELESS.2007.32

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[18] Oscar, T.P. (2009) General Regression Neural Network and Monte Carlo Simulation Model for Survival and Growth of Salmonella on Raw Chicken Skin as a Function of Serotype, Temperature and Time for Use in Risk Assessment. Journal of Food Protection, 72, 2078-2087.

[19] Liu, J. (2012) Predicting the Products of Crude Distillation Columns. Ph.D. Thesis, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester.

[20] Safdari, M. and Shamsoddini, M. (2012) Using Artificial Neural Networks and Monte Carlo Simulation in Terms of Uncertainty for Prediction of Budget Deficit in Iran. Interdisciplinary Journal of Contemporary Research in Business, 4, 132-139.

[21] Zilouchian, A. and Bawazir, K.H. (1999) Application of Neural Networks in Oil Refineries. Proceedings of IEEE International Conference on Neural Networks, New Orleans, 11-13 May 1999, 126-135.

[22] Liau, L.C.K., Yangb, T.C.K. and Tsaib, M.T. (2004) Expert System of a Crude Oil Distillation Unit for Process Optimization Using Neural Networks. Expert Systems with Applications, 26, 247-255.

[23] Motlaghi, S., Jalali, F. and Ahmadabadi, M.N. (2008) An Expert System Design for a Crude Oil Distillation Column with the Neural Networks Model and the Process Optimization using Genetic Algorithm Framework. Expert Systems with Applications, 35, 1540-1545.

http://dx.doi.org/10.1016/j.eswa.2007.08.105

[24] Tonnang, Z.E.H. (2010) Distillation Column Control Using Artificial Neural Networks. M. Sc Thesis, Microprocessors and Control Engineering, Department of Electrical and Electronics Engineering, Faculty of Technology, University of Ibadan, Ibadan.

[25] Popoola, L.T., Babagana, G. and Susu, A.A. (2013) Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery using Artificial Neural Network Model. International Journal of Research and Reviews in Applied Sciences, 15, 337-346.

[26] McCarthy, J. (1984) Some Expert System Need Common Sense. Stanford University, Stanford.

[27] Rich, E. (1994) Artificial Intelligence. McGraw Hill, New York.

[28] Miller, W. and Osborne, H.G. (1938) History and Development of Some Important Phases of Petroleum Refining in the United States. The Science of Petroleum, Oxford University Press, London, 1465-1477.

[29] Liebmann, K., Dhole, V.R. and Jobson, M. (1998) Integrated Design of a Conventional Crude Oil Distillation Tower Using Pinch Analysis. Chemical Engineering Research and Design, 76, 335-347.

http://dx.doi.org/10.1205/026387698524767

[30] Beer, M. and Spanos, P.D. (2005) Neural Network Based Monte Carlo Simulation of Random Processes. Proceedings of the 9th International Conference on Structural Safety and Reliability (ICOSSAR 2005), Millpress, Rotterdam, 9-16.

[31] Lee, S.C. (2003) Prediction of Concrete Strength Using Artificial Neural Networks. Engineering Structures, 25, 849-857.

http://dx.doi.org/10.1016/S0141-0296(03)00004-X

[32] Bishop, C.M. (1995) Neural Networks for Pattern Recognition. Clarendon Press, Oxford.

[33] Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River.

[34] de Freitas, J.F.G. (2001) Bayesian Methods for Neural Networks. Ph.D. Thesis, University of Cambridge, Cambridge.

[35] Neal, R.M. (1995) Bayesian Learning for Neural Networks. Ph.D. Thesis, Graduate Department of Computer Science, University of Toronto, Toronto.

[36] Fearnhead, P. (1998) Sequential Monte Carlo Methods in Filter Theory. Ph.D. Thesis, Department of Statistics, Oxford University, Oxford.

[37] Doucet, A. (1997) Monte Carlo Methods for Bayesian Estimation of Hidden Markov Models. Application to Radiation Signals. Ph.D. Thesis, University Paris-Sud, Orsay.

[38] Djuric, P.M. (1999) Monitoring and Selection of Dynamic Models by Monte Carlo Sampling. IEEE Higher Order Statistics Workshop, Ceasarea, 14-16 June 1999, 191-194.

[1] Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer Feedforward Networks are Universal Approximators. Neural Networks, 2, 359-366.

http://dx.doi.org/10.1016/0893-6080(89)90020-8

[2] Popoola, L.T., Babagana, G. and Susu, A.A. (2013) A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework. Advances in Chemical Engineering and Science, 3, 164-170.

http://dx.doi.org/10.4236/aces.2013.32020

[3] Rumelhart, D.E., Hinton, G.E.and Williams, R.J. (1986) Learning Internal Representation by Error Propagation. In: Parallel Distributed Processing: Exploration in the Microstructure of Cognition, MIT Press, Cambridge, 1, 318-361.

[4] Haykin, S. (1994) Neural Networks: A Comprehensive Foundation. Macmillan Publishing Company, New York.

[5] Hansen, L.K. and Solomon, P. (1990) Neural Network Ensembles. IEEE Trans. Pattern Analysis and Machine Intelligence, 12, 903-1002.

http://dx.doi.org/10.1109/34.58871

[6] Popoola, L.T., Babagana, G. and Susu, A.A. (2013) Thrombo-Embolic Stroke Prediction and Diagnosis Using Artificial Neural Network and Genetic Algorithm. IJRRAS, 14, 655-661.

[7] Firestone, M., Fenner-Crips, P., Barry, T. and Bennett, D. (1997) Guiding Principles for Monte Carlo Analysis. Risk Assessment Forum, US Environmental Protection Agency, Washington DC.

[8] Schueller, G.I. and Spanos, P.D. (2001) Monte Carlo Simulation. Proceedings of the International Conference on Monte Carlo Simulation, Lisse, 17-19 July 2001, 24-36.

[9] Zhang, X.G., Guo, X.Y., Zhong, B. and Peng, S.Y. (1998) Monte Carlo Simulation in Supercritical Methanol-Hexane System. Chinese Journal of Chemical Engineering, 14, 413-418.

[10] Alexander, N., Moyeed, R. and Stander, J. (2000) Spatial Modelling of Individual-Level Parasite Counts using the Negative Binomial Distribution. Biostatistics, 1, 453-463.

[11] Gilks, W. and Berzuini, C. (2001) Following a Moving Target: Monte Carlo Inference for Dynamic Bayesian Models. Journal of the Royal Statistical Society: Series B, 1, 127-146.

[12] Khu, S.T. and Werner, M.G.F. (2003) Reduction of Monte-Carlo Simulation Runs for Uncertainty Estimation in Hydrological Modelling. Hydrology and Earth System Sciences, 7, 680-692.

http://dx.doi.org/10.5194/hess-7-680-2003

[13] Kuo, R.J., Liao, J.L. and Tu, C. (2005) Integration of ART2 Neural Network and Genetic K-means Algorithm for Analyzing Web Browsing Paths in Electronic Commerce. Decision Support Systems, 40, 355-374.

http://dx.doi.org/10.1016/j.dss.2004.04.010

[14] Jia, Y.X. and Guo, X.Y. (2006) Monte Carlo Simulation of Methanol Diffusion in Critical Media. Chinese Journal of Chemical Engineering, 14, 413-418.

http://dx.doi.org/10.1016/S1004-9541(06)60093-1

[15] Yeh, W.C., Yu, C.Y. and Lin, C.H. (2007) Evaluate Voting System Reliability using the Monte Carlo Simulation and Artificial Neural Network. 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, Sydney, 27-30 August 2007, 45-49. http://dx.doi.org/10.1109/AUSWIRELESS.2007.32

[16] Sugiyama, S. (2008) Monte Carlo Simulation/Risk Analysis on a Spreadsheet: Review of Three Software Packages. Foresight: The International Journal of Applied Forecasting, 2008, 36-41.

[17] Pang, Z., Liu, D., Jin, N. and Wang, Z. (2008) A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem. IEEE Transactions on Circuits and Systems I: Regular Papers, 55, 3421-3429.

[18] Oscar, T.P. (2009) General Regression Neural Network and Monte Carlo Simulation Model for Survival and Growth of Salmonella on Raw Chicken Skin as a Function of Serotype, Temperature and Time for Use in Risk Assessment. Journal of Food Protection, 72, 2078-2087.

[19] Liu, J. (2012) Predicting the Products of Crude Distillation Columns. Ph.D. Thesis, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester.

[20] Safdari, M. and Shamsoddini, M. (2012) Using Artificial Neural Networks and Monte Carlo Simulation in Terms of Uncertainty for Prediction of Budget Deficit in Iran. Interdisciplinary Journal of Contemporary Research in Business, 4, 132-139.

[21] Zilouchian, A. and Bawazir, K.H. (1999) Application of Neural Networks in Oil Refineries. Proceedings of IEEE International Conference on Neural Networks, New Orleans, 11-13 May 1999, 126-135.

[22] Liau, L.C.K., Yangb, T.C.K. and Tsaib, M.T. (2004) Expert System of a Crude Oil Distillation Unit for Process Optimization Using Neural Networks. Expert Systems with Applications, 26, 247-255.

[23] Motlaghi, S., Jalali, F. and Ahmadabadi, M.N. (2008) An Expert System Design for a Crude Oil Distillation Column with the Neural Networks Model and the Process Optimization using Genetic Algorithm Framework. Expert Systems with Applications, 35, 1540-1545.

http://dx.doi.org/10.1016/j.eswa.2007.08.105

[24] Tonnang, Z.E.H. (2010) Distillation Column Control Using Artificial Neural Networks. M. Sc Thesis, Microprocessors and Control Engineering, Department of Electrical and Electronics Engineering, Faculty of Technology, University of Ibadan, Ibadan.

[25] Popoola, L.T., Babagana, G. and Susu, A.A. (2013) Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery using Artificial Neural Network Model. International Journal of Research and Reviews in Applied Sciences, 15, 337-346.

[26] McCarthy, J. (1984) Some Expert System Need Common Sense. Stanford University, Stanford.

[27] Rich, E. (1994) Artificial Intelligence. McGraw Hill, New York.

[28] Miller, W. and Osborne, H.G. (1938) History and Development of Some Important Phases of Petroleum Refining in the United States. The Science of Petroleum, Oxford University Press, London, 1465-1477.

[29] Liebmann, K., Dhole, V.R. and Jobson, M. (1998) Integrated Design of a Conventional Crude Oil Distillation Tower Using Pinch Analysis. Chemical Engineering Research and Design, 76, 335-347.

http://dx.doi.org/10.1205/026387698524767

[30] Beer, M. and Spanos, P.D. (2005) Neural Network Based Monte Carlo Simulation of Random Processes. Proceedings of the 9th International Conference on Structural Safety and Reliability (ICOSSAR 2005), Millpress, Rotterdam, 9-16.

[31] Lee, S.C. (2003) Prediction of Concrete Strength Using Artificial Neural Networks. Engineering Structures, 25, 849-857.

http://dx.doi.org/10.1016/S0141-0296(03)00004-X

[32] Bishop, C.M. (1995) Neural Networks for Pattern Recognition. Clarendon Press, Oxford.

[33] Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River.

[34] de Freitas, J.F.G. (2001) Bayesian Methods for Neural Networks. Ph.D. Thesis, University of Cambridge, Cambridge.

[35] Neal, R.M. (1995) Bayesian Learning for Neural Networks. Ph.D. Thesis, Graduate Department of Computer Science, University of Toronto, Toronto.

[36] Fearnhead, P. (1998) Sequential Monte Carlo Methods in Filter Theory. Ph.D. Thesis, Department of Statistics, Oxford University, Oxford.

[37] Doucet, A. (1997) Monte Carlo Methods for Bayesian Estimation of Hidden Markov Models. Application to Radiation Signals. Ph.D. Thesis, University Paris-Sud, Orsay.

[38] Djuric, P.M. (1999) Monitoring and Selection of Dynamic Models by Monte Carlo Sampling. IEEE Higher Order Statistics Workshop, Ceasarea, 14-16 June 1999, 191-194.