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Research Articles

Machine learning-assisted methods for prediction and optimization of oxidative desulfurization of gas condensate via a novel oxidation system

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Pages 84-100 | Received 26 Jan 2023, Accepted 31 Aug 2023, Published online: 18 Sep 2023

References

  • Wang H, Asif Amjad M, Arshed N, et al. Fossil energy demand and economic development in BRICS countries. Front. Energy Res. 2022;10:842793. doi:10.3389/fenrg.2022.842793
  • Quadrelli R, Peterson S. The energy–climate challenge: recent trends in CO2 emissions from fuel combustion. Energy Policy. 2007;35:5938–5952. doi:10.1016/j.enpol.2007.07.001
  • Ahmadpour J, Ahmadi M, Javdani A. Hydrodesulfurization unit for natural gas condensate. J Therm Anal Calorim. 2019;135:1943–1949. doi:10.1007/s10973-018-7512-4
  • Demirbas A, Alidrisi H, Balubaid MA. API gravity, sulfur content, and desulfurization of crude oil. Pet Sci Technol. 2015;33:93–101. doi:10.1080/10916466.2014.950383
  • Kruszewski Ł, Fabiańska MJ, Ciesielczuk J, et al. First multi-tool exploration of a gas-condensate-pyrolysate system from the environment of burning coal mine heaps: An in situ FTIR and laboratory GC and PXRD study based on upper silesian materials. Sci Total Environ. 2018;640-641:1044–1071. doi:10.1016/j.scitotenv.2018.05.319
  • Al-Jamimi HA, Al-Azani S, Saleh TA. Supervised machine learning techniques in the desulfurization of oil products for environmental protection: a review. Process Saf Environ Prot. 2018;120:57–71. doi:10.1016/j.psep.2018.08.021
  • Song C, Ma X. New design approaches to ultra-clean diesel fuels by deep desulfurization and deep dearomatization. Appl Catal B Environ. 2003;41:207–238. doi:10.1016/S0926-3373(02)00212-6
  • Campos-Martin JM, Capel-Sanchez MdC, Perez-Presas P, et al. Oxidative processes of desulfurization of liquid fuels. J Chem Technol Biotechnol. 2010;85:879–890. doi:10.1002/jctb.2371
  • Saleh TA. Advanced nanomaterials for water engineering, treatment, and hydraulics. IGI Global; 2017.
  • Saleh TA. Characterization, determination and elimination technologies for sulfur from petroleum: toward cleaner fuel and a safe environment. Trends Environ Anal Chem. 2020;25:e00080. doi:10.1016/j.teac.2020.e00080
  • Alibolandi M, Ghaedian M, Shafeghat A, et al. Oxidative desulfurization of sour Gas condensate and optimization of parameters with response surface methodology. J Sci Islam Repub Iran. 2020;31:13–23.
  • Pouladi B, Fanaei MA, Baghmisheh G. Optimization of oxidative desulfurization of gas condensate via response surface methodology approach. J Clean Prod [Internet]. 2019;209:965–977. Available from https://www.sciencedirect.com/science/article/pii/S095965261833316X.
  • Saleh TA. Carbon nanotube-incorporated alumina as a support for MoNi catalysts for the efficient hydrodesulfurization of thiophenes. Chem Eng J. 2021;404:126987), doi:10.1016/j.cej.2020.126987
  • Dehkordi AM, Sobati MA, Nazem MA. Oxidative desulfurization of non-hydrotreated kerosene using hydrogen peroxide and acetic acid. Chinese J Chem Eng. 2009;17:869–874. doi:10.1016/S1004-9541(08)60289-X
  • Jorjani E, Chelgani SC, Mesroghli SH. Application of artificial neural networks to predict chemical desulfurization of tabas coal. Fuel. 2008;87:2727–2734. doi:10.1016/j.fuel.2008.01.029
  • HA A-J, BinMakhashen GM, Saleh TA. Artificial intelligence approach for modeling petroleum refinery catalytic desulfurization process. Neural Comput Appl. 2022;34:17809–17820. doi:10.1007/s00521-022-07423-x
  • Salari D, Rostamizadeh K. Oxidative desulfurization of fuel oil: modeling based on artificial neural network. Pet Sci Technol. 2008;26:382–397. doi:10.1080/10916460600809592
  • Zadeh LA. Fuzzy sets. Inf Control. 1965;8:338–353. doi:10.1016/S0019-9958(65)90241-X
  • Khosravi A, Syri S, Pabon JJG, et al. Energy modeling of a solar dish/stirling by artificial intelligence approach. Energy Convers Manag [Internet]. 2019;199:112021. Available from: https://www.sciencedirect.com/science/article/pii/S0196890419310271.
  • Rahmanian B, Pakizeh M, Esfandyari M, et al. Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF). J Hazard Mater. 2011: 192:585–592. doi:10.1016/j.jhazmat.2011.05.051
  • Esfandyari M, Esfandyari M, Jafari D. Prediction of thiophene removal from diesel using [BMIM][AlCl4]. in EDS Process: Ga-ANFIS and PSO-ANFIS Modeling. Pet Sci Technol. 2018;36:1305–1311.
  • Nwosu-Obieogu K, Dzarma GW, Ehimogue P, et al. Textile wastewater heavy metal removal using luffa cylindrica activated carbon: an ANN and ANFIS predictive model evaluation. Appl Water Sci. 2022;12:1–11. doi:10.1007/s13201-022-01575-w
  • Dirik M. Prediction of NOx emissions from gas turbines of a combined cycle power plant using an ANFIS model optimized by GA. Fuel. 2022;321:124037), doi:10.1016/j.fuel.2022.124037
  • Ao Y, Laghrouche S, Depernet D. Diagnosis of proton exchange membrane fuel cell system based on adaptive neural fuzzy inference system and electrochemical impedance spectroscopy. Energy Convers Manag [Internet]. 2022;256:115391. Available from: https://www.sciencedirect.com/science/article/pii/S019689042200187X.
  • Dolatabadi M, Mehrabpour M, Esfandyari M, et al. Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS. Chemom Intell Lab Syst. 2018;181:72–78. doi:10.1016/j.chemolab.2018.07.012
  • Esfandyari M, Fanaei MA, Gheshlaghi R, et al. Neural network and neuro-fuzzy modeling to investigate the power density and columbic efficiency of microbial fuel cell. J Taiwan Inst Chem Eng. 2016: 58:84–91. doi:10.1016/j.jtice.2015.06.005
  • Holland JH. Genetic algorithms and adaptation. Adapt Control ill-Defined Syst. 1984: 317–333. doi:10.1007/978-1-4684-8941-5_21
  • Chen W, Panahi M, Pourghasemi HR. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena. 2017;157:310–324. doi:10.1016/j.catena.2017.05.034
  • Koolivand-Salooki M, Esfandyari M, Rabbani E, et al. Application of genetic programing technique for predicting uniaxial compressive strength using reservoir formation properties. J Pet Sci Eng. 2017: 159:35–48. doi:10.1016/j.petrol.2017.09.032

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