Prediction of Desulfurization from Diesel of Isfahan Refinery by Membrane Method Using Intelligent Approach

Document Type : Research Article

Authors

1 Faculty of Engineering, University of Kashan, Kashan, I.R. IRAN

2 Faculty of Chemical and Materials Engineering, Shahroud University of Technology, Shahroud, I.R. IRAN

3 Department of Chemical Engineering, Faculty of Engineering, Golestan University, Gorgan, I.R. IRAN

Abstract

In this study, modeling and investigation of desulfurization of diesel Isfahan Oil Refinery Company were performed using membrane process. The total sulfur of diesel as feed to membrane module is 6380ppm. In the research, four kinds of membrane including Polydimethylsiloxane, blend of Polydimethylsiloxane with Polyethylene glycol, blend of Polydimethylsiloxane with Polyethersulfone and blend of Polydimethylsiloxane with Polyacrylonitrile are used. The process variables in this research are pump pressure of membrane module (5-9 bar), crosslinking agent concentration (1.5 and 3 wt. %), crosslinking temperature (65-85°ċ), and crosslinking time (0.5-2.5 h). In the modeling procedure, Artificial Neural Network (ANN) and genetic programming (GP) were employed. The Levenberg-Marquardt training algorithm was used to train the ANN. ANN architecture with 6 neurons was determined as optimal architecture. ANN and GP are beneficial tools for predicting the performance of RO with high accuracy (R2=0.93 and 0.89 respectively).

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References

[1] شفقت قائدیان.، محرابی امین.، کاهش گوگرد گازوییل به کمک امواج اولتراسونیک، پژوهش نفت، 24: 85 تا 95 (1393).
[2] Abdullah W.N.W., "Preparation Characterization and Mechanistic Study of Alumina Supported Polymolybdate Based Catalysts for Catalytic Oxidative Desulfurization of Diesel Fuel", Universiti Teknologi Malaysia (2016).
[3] Bhutto A.W., Abro R., Gao S., Abbas T., Chen X., Yu G., Oxidative Desulfurization of Fuel Oils Using Ionic Liquids: A Review, Journal of the Taiwan Institute of Chemical Engineers, 62: 84-97 (2016).
[4] Gao S., Yu G., Abro R., Abdeltawab A.A., Al-Deyab S.S., Chen X., Desulfurization of Fuel Oils: Mutual Solubility of Ionic Liquids and Fuel Oil, Fuel, 173: 164-171 (2016).
[5] Dharaskar S.A., Wasewar K.L., Varma M.N., Shende D.Z., Yoo C., Synthesis, Characterization and Application of 1-Butyl-3-Methylimidazolium Tetrafluoroborate for Extractive Desulfurization of Liquid Fuel, Arabian Journal of Chemistry, 9: 578-587 (2016).
[6] Khaled M.M., Nazal M.K., Atieh M.A., Method for Desulfurizing Diesel Fuel, in, Google Patents, (2018).
[7] Qi R., Wang Y., Chen J., Li J., Zhu S., Removing Thiophenes from N-Octane Using PDMS–AgY Zeolite Mixed Matrix Membranes, Journal of Membrane Science, 295: 114-120 (2007).
[8]Padaki M., Murali R.S., Abdullah M.S., Misdan N., Moslehyani A., Kassim M., N., Hilal Ismail A., Membrane Technology Enhancement in Oil–Water Separation. A Review, Desalination, 357: 197-207 (2015).
[9] Nath K., “Membrane Separation Processes”, PHI Learning Pvt. Ltd., (2017).
[10] Rezakazemi Marjani, S. Shirazian M. A., Organic Solvent Removal by Pervaporation Membrane Technology: Experimental and Simulation, Environmental Science and Pollution Research, 20: 1-8 (2018) .
[11] Sha S., Kong Y., Yang J., The Pervaporation Performance of C60-Filled Ethyl Cellulose Hybrid Membrane for Gasoline Desulfurization: Effect of Operating Temperature, Energy & Fuels, 26: 6925-6929 (2012).
[12] Jain M., Attarde D., Gupta S.K., Influence of Hydrocarbon Species on the Removal of Thiophene from FCC Gasoline by Using a Spiral Wound Pervaporation Module, Journal of Membrane Science, 507: 43-54 (2016).
[13] Zhang Q.G., Fan B.C., Liu Q.L., Zhu A.M., Shi F.F., A Novel Poly (Dimethyl Siloxane)/Poly (Oligosilsesquioxanes) Composite Membrane for Pervaporation Desulfurization, Journal of Membrane Science, 366: 335-341(2011).
[14] Sobana S., Panda R.C., Identification, Modelling, and Control of Continuous Reverse Osmosis Desalination System: A Review, Separation Science and Technology, 46: 551-560 (2011).
[15] Rezakazemi M., Dashti A., Asghari M., Shirazian S., H2-Selective Mixed Matrix Membranes Modeling using ANFIS, PSO-ANFIS, GA-ANFIS, International Journal of Hydrogen Energy, 42: 15211-15225 (2017).
[16] Rezakazemi M., Azarafza A., Dashti A., Shirazian S., Development of Hybrid Models for Prediction of Gas Permeation Through FS/POSS/PDMS Nanocomposite Membranes, International Journal of Hydrogen Energy, 43: 17283-17294 (2018).
[17] Lee T.-M., Oh H., Choung Y.-K., Oh S., Jeon M., Kim J.H., Nam S.H., Lee S., Prediction of Membrane Fouling in the Pilot-Scale Microfiltration System Using Genetic Programming, Desalination, 247: 285-294 (2009).
[18] Shokrkar H., Salahi A., Kasiri N., Mohammadi T., Prediction of Permeation Flux Decline During MF of Oily Wastewater Using Genetic Programming, Chemical Engineering Research and Design, 90: 846-853 (2012).
[19] Sharma S., Sandal K., Garg P., Sharma S.D., “Performance Analysis of Window Functions for Exon Prediction in DNA Sequences”, in  Computing, Communication and Automation (ICCCA), 2017 International Conference, IEEE 283-286 (2017).
[20] Sarkar B., Sengupta A., De S., DasGupta S., Prediction of Permeate Flux During Electric Field Enhanced Cross-Flow Ultrafiltration—a Neural Network Approach, Separation and Purification Technology, 65: 260-268 (2009).
[21] Farno E., Rezakazemi M., Mohammadi T., Kasiri N., Ternary Gas Permeation Through Synthesized Pdms Membranes: Experimental and CFD Simulation Based on Sorption‐Dependent System Using Neural Network Model, Polymer Engineering & Science, 54: 215-226 (2014).
[22] Prochazka A., Kingsbury N., Payner P., Uhlir J., “Signal Analysis and Prediction”, Springer Science & Business Media, (2013).
[23] Koza J.R., ‘Genetic Programming II, Automatic Discovery of Reusable Subprograms”, MIT Press, Cambridge, MA, (1992).
[24] Farsi M., Heidarinasab A., Honarvar B., Arjmand, M., Desulfurization of Disle Isfahan Oil Refinery Company by Polydimethylsiloxane, Poly Ethylene Glycol, Polyethersulfone and Poly Acryl Nitrile Nanocomposites, Life, 50: 22-36 (2017).
[25] Grosman B., Lewin D.R., Automated Nonlinear Model Predictive Control using Genetic Programming, Computers & Chemical Engineering, 26: 631-640 (2002).
[26] Das A., Abdel-Aty M., A Genetic Programming Approach to Explore the Crash Severity on Multi-Lane Roads, Accident Analysis & Prevention, 42: 548-557 (2010).
[27] Lin L., Kong Y., Zhang Y., Sorption and Transport Behavior of Gasoline Components in Polyethylene Glycol Membranes, Journal of Membrane Science, 325: 438-445 (2008).
[28] Lin L., Wang G., Qu H., Yang J., Wang Y., Shi D., Kong Y., Pervaporation Performance of Crosslinked Polyethylene Glycol Membranes for Deep Desulfurization of FCC Gasoline, Journal of Membrane Science, 280: 651-658 (2006).
Nashrieh Shimi va Mohandesi Shimi Iran
Volume 39, Issue 3 - Serial Number 97
September 2020
Pages 237-246

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