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Investigation of Asphaltene Precipitation Using Response Surface Methodology Combined with Artificial Neural Network | ||
| Journal of Chemical and Petroleum Engineering | ||
| مقاله 2، دوره 53، شماره 2، اسفند 2019، صفحه 153-167 اصل مقاله (996.48 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jchpe.2019.261438.1238 | ||
| نویسندگان | ||
| Zeinab Hosseini-dastgerdi1؛ Saeid Jafarzadeh-Ghoushchi* 2 | ||
| 1Faculty of Chemical Engineering, Urmia University of Technology, Urmia, Iran | ||
| 2Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran | ||
| چکیده | ||
| The precipitation of asphaltene, one of the components of oil, in reservoirs, transfer lines, and equipment causes many problems. Accordingly, researchers are prompted to determine the factors affecting asphaltene precipitation and methods of avoiding its formation. Predicting precipitation and examining the simultaneous effect of operational variables on asphaltene precipitation are difficult because of the multiplicity, complexity, and nonlinearity of factors affecting asphaltene precipitation and the high cost of experiments. This study combined the use of response surface methodology and the artificial neural network to predict asphaltene precipitation under the mutual effects of various parameters. The values of such parameters were determined to reach the minimum amount of precipitation. We initially selected the appropriate algorithm for predicting asphaltene precipitation from the two neural network algorithms. The outputs of designed experiments in response surface methodology were determined using the optimum algorithm of the neural network. The effects of variables on asphaltene precipitation were then investigated by response surface methodology. According to the results, the minimum precipitation of asphaltene achieved at zero mole percent of injected nitrogen and methane, 10–20 mole percent of injected carbon dioxide, asphaltene content of 0.46, the resin content of 16.8 weight percent, the pressure of 333 psi, and temperature of 180 . Results showed that despite the complexities of asphaltene precipitation, the combination of artificial neural network with response surface methodology can be successfully used to investigate the mutual effect of different variables affecting asphaltene precipitation. | ||
| کلیدواژهها | ||
| Artificial Neural Network Asphaltene؛ Desirability؛ Precipitation؛ Response Surface Methodology | ||
| مراجع | ||
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[1] Hammami A, Ratulowski J. Precipitation and deposition of asphaltenes in production systems: a flow assurance overview. Mullins OC, Sheu EY, Hammami A, Marshall AG, (eds). Asphaltenes, Heavy Oils, and Petroleomics. New York, NY: Springer; 2007.
[2] Mullins OC, Sabbah H, Eyssautier J, Pomerantz AE, Barré L, Andrews AB, Ruiz-Morales Y, Mostowfi F, McFarlane R, Goual L, Lepkowicz R. Advances in asphaltene science and the Yen–Mullins model. Energy & Fuels. 2012 May 8;26(7):3986-4003.
[3] Hosseini-Dastgerdi Z, Meshkat SS. An experimental and modeling study of asphaltene adsorption by carbon nanotubes from model oil solution. Journal of Petroleum Science and Engineering. 2019 Mar 1;174:1053-61.
[4] Dastgerdi ZH, Meshkat SS, Hosseinzadeh S, Esrafili MD. Application of Novel Fe3O4–Polyaniline Nanocomposites in Asphaltene Adsorptive Removal: Equilibrium, Kinetic Study and DFT Calculations. Journal of Inorganic and Organometallic Polymers and Materials. 2019 Jul 1;29(4):1160-70.
[5] Hosseini‐Dastgerdi Z, Tabatabaei‐Nejad SA, Khodapanah E, Sahraei E. A comprehensive study on mechanism of formation and techniques to diagnose asphaltene structure; molecular and aggregates: a review. Asia‐Pacific Journal of Chemical Engineering. 2015 Jan;10(1):1-14.
[6] Hosseini-Dastgerdi Z, Tabatabaei-Nejad SA, Sahraei E, Nowroozi H. Morphology and size distribution characterization of precipitated asphaltene from live oil during pressure depletion. Journal of Dispersion Science and Technology. 2015 Mar 4;36(3):363-8.
[7] Akbarzadeh K, Hammami A, Kharrat A, Zhang D, Allenson S, Creek J, Kabir S, Jamaluddin A, Marshall AG, Rodgers RP, Mullins OC. Asphaltenes—problematic but rich in potential. Oilfield Review. 2007 Jul;19(2):22-43.
[8] Tavakkoli M, Panuganti SR, Taghikhani V, Pishvaie MR, Chapman WG. Precipitated asphaltene amount at high-pressure and high-temperature conditions. Energy & Fuels. 2013 Nov 20;28(3):1596-610.
[9] Zanganeh P, Ayatollahi S, Alamdari A, Zolghadr A, Dashti H, Kord S. Asphaltene deposition during CO2 injection and pressure depletion: a visual study. Energy & Fuels. 2012 Jan 13;26(2):1412-9.
[10] Syunyaev RZ, Likhatsky VV. Effects of temperature and pressure on the phase state of oils and asphaltene solutions observed using dielectric spectroscopy. Energy & Fuels. 2010 Jan 20;24(4):2233-9.
[11] AlHammadi AA, Chen Y, Yen A, Wang J, Creek JL, Vargas FM, Chapman WG. Effect of the gas composition and gas/oil ratio on asphaltene deposition. Energy & Fuels. 2017 Mar 10;31(4):3610-9.
[12] Zanganeh P, Dashti H, Ayatollahi S. Visual investigation and modeling of asphaltene precipitation and deposition during CO2 miscible injection into oil reservoirs. Fuel. 2015 Nov 15;160:132-9.
[13] Wang P, Zhao F, Hou J, Lu G, Zhang M, Wang Z. Comparative Analysis of CO2, N2, and Gas Mixture Injection on Asphaltene Deposition Pressure in Reservoir Conditions. Energies. 2018 Sep;11(9):2483-97
[14] Mullins OC. The modified Yen model. Energy & Fuels. 2010 Jan 19;24(4):2179-207.
[15] Anisimov MA, Ganeeva YM, Gorodetskii EE, Deshabo VA, Kosov VI, Kuryakov VN, Yudin DI, Yudin IK. Effects of resins on aggregation and stability of asphaltenes. Energy & Fuels. 2014 Sep 24;28(10):6200-9.
[16] Li Z, Firoozabadi A. Cubic-plus-association equation of state for asphaltene precipitation in live oils. Energy & fuels. 2010 Apr 5;24(5):2956-63.
[17] Dehaghani YH, Assareh M, Feyzi F. Asphaltene precipitation modeling with PR and PC-SAFT equations of state based on normal alkanes titration data in a Multisolid approach. Fluid Phase Equilibria. 2018 Aug 25;470:212-20.
[18] Merino‐Garcia D, Correra S. A shortcut application of a Flory‐like model to Asphaltene precipitation. Journal of Dispersion Science and Technology. 2007 Mar 1;28(3):339-47.
[19] Manshad AK, Edalat M. Application of continuous polydisperse molecular thermodynamics for modeling asphaltene precipitation in crude oil systems. Energy & Fuels. 2008 May 22;22(4):2678-86.
[20] Arya A, Liang X, Von Solms N, Kontogeorgis GM. Modeling of asphaltene onset precipitation conditions with cubic plus association (CPA) and perturbed chain statistical associating fluid theory (PC-SAFT) equations of state. Energy & Fuels. 2016 Jul 26;30(8):6835-52.
[21] Gharagheizi F, Eslamimanesh A, Sattari M, Mohammadi AH, Richon D. Corresponding states method for evaluation of the solubility parameters of chemical compounds. Industrial & Engineering Chemistry Research. 2012 Feb 22;51(9):3826-31.
[22] Pazuki GR, Nikookar M. A modified Flory-Huggins model for prediction of asphaltenes precipitation in crude oil. Fuel. 2006 May 1;85(7-8):1083-6.
[23] Yazdizadeh M, Nourbakhsh H, Jafari Nasr MR. A Solution Model for Predicting Asphaltene Precipitation. Iranian Journal of Chemistry and Chemical Engineering (IJCCE). 2014 Mar 1;33(1):93-102.
[24] Ahmadi M, Jafarzadeh-Ghoushchi S, Taghizadeh R, Sharifi A. Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches. Neural Computing and Applications.:1-20.
[25] Salem S, Jafarzadeh-Ghoushchi S. Estimation of optimal physico-chemical characteristics of nano-sized inorganic blue pigment by combined artificial neural network and response surface methodology. Chemometrics and Intelligent Laboratory Systems. 2016 Dec 15;159:80-8.
[26] Jafarzadeh-Ghoushchi S, Rahman MN. Performance study of artificial neural network modelling to predict carried weight in the transportation system. International Journal of Logistics Systems and Management. 2016;24(2):200-12.
[27] Al Ameen A, Mondal S, Pudi SM, Pandhare NN, Biswas P. Liquid phase hydrogenolysis of glycerol over highly active 50% Cu-Zn (8: 2)/MgO catalyst: reaction parameter optimization by using response surface methodology. Energy & Fuels. 2017;31(8):8521-33.
[28] Ahmadi MA, Golshadi M. Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. Journal of Petroleum Science and Engineering. 2012 Nov 1;98:40-9.
[29] Abedini A, Ashoori S, Torabi F, Saki Y, Dinarvand N. Mechanism of the reversibility of asphaltene precipitation in crude oil. Journal of Petroleum Science and Engineering. 2011 Aug 1;78(2):316-20.
[30] Khamehchi E, Behvandi R. Prediction of Bubble Point Pressure & Asphaltene Onset Pressure During CO2 Injection Using ANN & ANFIS Models. Journal of Petroleum Science and Technology. 2011 Aug 10;1(2):35-45.
[31] Ashoori S, Abedini A, Abedini R, Nasheghi KQ. Comparison of scaling equation with neural network model for prediction of asphaltene precipitation. Journal of Petroleum Science and Engineering. 2010 May 1;72(1-2):186-94.
[32] Tavakkoli M, Kharrat R, Masihi M, Ghazanfari MH, Fadaei S. Phase behavior modeling of asphaltene precipitation for heavy crudes: a promising tool along with experimental data. International Journal of Thermophysics. 2012 Dec 1;33(12):2251-66.
[33] Moradi S, Rashtchian D, Ganjeh Ghazvini M, Emadi MA, Dabir B. Experimental investigation and modeling of asphaltene precipitation due to gas injection. Iranian Journal of Chemistry and Chemical Engineering (IJCCE). 2012 Mar 1;31(1):89-98.
[34] Nakhli H, Alizadeh A, Moqadam MS, Afshari S, Kharrat R, Ghazanfari MH. Monitoring of asphaltene precipitation: Experimental and modeling study. Journal of Petroleum Science and Engineering. 2011 Aug 1;78(2):384-95.
[35] Afshari S, Kharrat R, Ghazanfari MH. Asphaltene precipitation study during natural depletion at reservoir conditions. InInternational Oil and Gas Conference and Exhibition in China 2010 Jan 1. Society of Petroleum Engineers.
[36] Moradi S, Dabiri M, Dabir B, Rashtchian D, Emadi MA. Investigation of asphaltene precipitation in miscible gas injection processes: experimental study and modeling. Brazilian Journal of Chemical Engineering. 2012 Sep;29(3):665-76.
[37] Kord S, Ayatollahi S. Asphaltene precipitation in live crude oil during natural depletion: experimental investigation and modeling. Fluid Phase Equilibria. 2012 Dec 25;336:63-70.
[38] Alizadeh A, Nakhli H, Kharrat R, Ghazanfari MH, Aghajani M. Experimental study of asphaltene precipitation behavior during miscible carbon dioxide injection. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2014 Jul 18;36(14):1523-30.
[39] Bahrami P, Kharrat R, Mahdavi S, Firoozinia H. Prediction of the gas injection effect on the asphaltene phase envelope. Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles. 2015 Nov 1;70(6):1075-86.
[40] Alizadeh A, Nakhli H, Kharrat R, Ghazanfari MH. An experimental investigation of asphaltene precipitation during natural production of heavy and light oil reservoirs: The role of pressure and temperature. Petroleum Science and Technology. 2011 Mar 16;29(10):1054-65.
[41] Jafarzadeh SG, Rahman MN, Wahab DA. Forecasting capabilities of spare part production with artificial neural networks model in a supply chain. World Applied Sciences Journal. 2012;20(5):674-8.
[42] Jafarzadeh SG, Rahman MN, Wahab DA. Optimization of supply chain management based on response surface methodology: A case study of iran khodro. World Applied Sciences Journal. 2012;20(4):620-7.
[43] Ahmadi MA. Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. Journal of Petroleum Exploration and Production Technology. 2011 Dec 1;1(2-4):99-106.
[44] Khayet M, Cojocaru C, García-Payo C. Application of response surface methodology and experimental design in direct contact membrane distillation. Industrial & engineering chemistry research. 2007 Aug 15;46(17):5673-85.
[45] Mohammadi M, Dadvar M, Dabir B. Application of response surface methodology for optimization of the stability of asphaltene particles in crude oil by TiO2/SiO2 nanofluids under static and dynamic conditions. Journal of Dispersion Science and Technology. 2018 Mar 4;39(3):431-42. | ||
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