{"title":"离子液体中气态碳氢化合物的智能溶解度估算","authors":"Behnaz Basirat , Fariborz Shaahmadi , Seyed Sorosh Mirfasihi , Abolfazl Jomekian , Bahamin Bazooyar","doi":"10.1016/j.petlm.2023.09.002","DOIUrl":null,"url":null,"abstract":"<div><p>The research focuses on evaluating how well new solvents attract light hydrocarbons, such as propane, methane, and ethane, in natural gas sweetening units. It is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening process. To address this challenge, the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network (ML-ANN), Support Vector Machines (SVM), and Least Square Support Vector Machine (LSSVM). The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Complex Evolution (SCE). Data on the solubility of propane, methane, and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive database. The proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic liquids. Among these, the hybrid SVM models perform exceptionally well, with the PSO-SVM hybrid model being particularly efficient computationally. To ensure a comprehensive analysis, different examples of hydrocarbons and their order are included. Additionally, a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility analysis. The results demonstrate the superiority of the AI models, as they outperform traditional thermodynamic models across a wide range of data. In conclusion, this study introduces advanced artificial intelligence algorithms such as ML-ANN, SVM, and LSSVM in accurately estimating the solubility of hydrocarbons in ionic liquids. The incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy, precision, and reliability of these intelligent models. These findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynamic models.</p></div>","PeriodicalId":37433,"journal":{"name":"Petroleum","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405656123000548/pdfft?md5=664c48093d6d4fe643139268259683f4&pid=1-s2.0-S2405656123000548-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Intelligent solubility estimation of gaseous hydrocarbons in ionic liquids\",\"authors\":\"Behnaz Basirat , Fariborz Shaahmadi , Seyed Sorosh Mirfasihi , Abolfazl Jomekian , Bahamin Bazooyar\",\"doi\":\"10.1016/j.petlm.2023.09.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The research focuses on evaluating how well new solvents attract light hydrocarbons, such as propane, methane, and ethane, in natural gas sweetening units. It is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening process. To address this challenge, the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network (ML-ANN), Support Vector Machines (SVM), and Least Square Support Vector Machine (LSSVM). The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Complex Evolution (SCE). Data on the solubility of propane, methane, and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive database. The proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic liquids. Among these, the hybrid SVM models perform exceptionally well, with the PSO-SVM hybrid model being particularly efficient computationally. To ensure a comprehensive analysis, different examples of hydrocarbons and their order are included. Additionally, a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility analysis. The results demonstrate the superiority of the AI models, as they outperform traditional thermodynamic models across a wide range of data. In conclusion, this study introduces advanced artificial intelligence algorithms such as ML-ANN, SVM, and LSSVM in accurately estimating the solubility of hydrocarbons in ionic liquids. The incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy, precision, and reliability of these intelligent models. These findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynamic models.</p></div>\",\"PeriodicalId\":37433,\"journal\":{\"name\":\"Petroleum\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2405656123000548/pdfft?md5=664c48093d6d4fe643139268259683f4&pid=1-s2.0-S2405656123000548-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Petroleum\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2405656123000548\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Petroleum","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405656123000548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Intelligent solubility estimation of gaseous hydrocarbons in ionic liquids
The research focuses on evaluating how well new solvents attract light hydrocarbons, such as propane, methane, and ethane, in natural gas sweetening units. It is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening process. To address this challenge, the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network (ML-ANN), Support Vector Machines (SVM), and Least Square Support Vector Machine (LSSVM). The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Complex Evolution (SCE). Data on the solubility of propane, methane, and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive database. The proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic liquids. Among these, the hybrid SVM models perform exceptionally well, with the PSO-SVM hybrid model being particularly efficient computationally. To ensure a comprehensive analysis, different examples of hydrocarbons and their order are included. Additionally, a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility analysis. The results demonstrate the superiority of the AI models, as they outperform traditional thermodynamic models across a wide range of data. In conclusion, this study introduces advanced artificial intelligence algorithms such as ML-ANN, SVM, and LSSVM in accurately estimating the solubility of hydrocarbons in ionic liquids. The incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy, precision, and reliability of these intelligent models. These findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynamic models.
期刊介绍:
Examples of appropriate topical areas that will be considered include the following: 1.comprehensive research on oil and gas reservoir (reservoir geology): -geological basis of oil and gas reservoirs -reservoir geochemistry -reservoir formation mechanism -reservoir identification methods and techniques 2.kinetics of oil and gas basins and analyses of potential oil and gas resources: -fine description factors of hydrocarbon accumulation -mechanism analysis on recovery and dynamic accumulation process -relationship between accumulation factors and the accumulation process -analysis of oil and gas potential resource 3.theories and methods for complex reservoir geophysical prospecting: -geophysical basis of deep geologic structures and background of hydrocarbon occurrence -geophysical prediction of deep and complex reservoirs -physical test analyses and numerical simulations of reservoir rocks -anisotropic medium seismic imaging theory and new technology for multiwave seismic exploration -o theories and methods for reservoir fluid geophysical identification and prediction 4.theories, methods, technology, and design for complex reservoir development: -reservoir percolation theory and application technology -field development theories and methods -theory and technology for enhancing recovery efficiency 5.working liquid for oil and gas wells and reservoir protection technology: -working chemicals and mechanics for oil and gas wells -reservoir protection technology 6.new techniques and technologies for oil and gas drilling and production: -under-balanced drilling/gas drilling -special-track well drilling -cementing and completion of oil and gas wells -engineering safety applications for oil and gas wells -new technology of fracture acidizing