离子液体中气态碳氢化合物的智能溶解度估算

IF 4.2 Q2 ENERGY & FUELS
Behnaz Basirat , Fariborz Shaahmadi , Seyed Sorosh Mirfasihi , Abolfazl Jomekian , Bahamin Bazooyar
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引用次数: 0

摘要

研究重点是评估新溶剂在天然气增甜装置中吸引轻烃(如丙烷、甲烷和乙烷)的能力。准确确定碳氢化合物在这些溶剂中的溶解度对于有效管理增甜工艺非常重要。为应对这一挑战,研究建议使用基于人工智能技术的先进经验模型,如多层人工神经网络 (ML-ANN)、支持向量机 (SVM) 和最小平方支持向量机 (LSSVM)。SVM 和 LSSVM 模型的参数使用遗传算法(GA)、粒子群优化(PSO)和洗牌复杂进化(SCE)等优化方法进行估计。从可靠的文献来源收集了丙烷、甲烷和乙烷在各种离子液体中的溶解度数据,以创建一个全面的数据库。所提出的人工智能模型在预测碳氢化合物在离子液体中的溶解度方面显示出极高的准确性。其中,混合 SVM 模型的表现尤为突出,PSO-SVM 混合模型的计算效率尤其高。为了确保分析的全面性,本文中包含了碳氢化合物及其阶次的不同示例。此外,还进行了比较分析,将人工智能模型与用于溶解度分析的热力学 COSMO-RS 模型进行了比较。结果证明了人工智能模型的优越性,因为它们在广泛的数据范围内都优于传统的热力学模型。总之,本研究引入了先进的人工智能算法,如 ML-ANN、SVM 和 LSSVM,用于准确估算碳氢化合物在离子液体中的溶解度。优化技术和碳氢化合物实例的变化提高了这些智能模型的准确性、精确性和可靠性。这些发现凸显了基于人工智能的方法在溶解度分析中的巨大潜力,并强调了它们相对于传统热力学模型的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Petroleum
Petroleum Earth and Planetary Sciences-Geology
CiteScore
9.20
自引率
0.00%
发文量
76
审稿时长
124 days
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
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