Intelligent solubility estimation of gaseous hydrocarbons in ionic liquids

Abstract

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.

Keywords

Solubility / Gaseous hydrocarbon / Intelligent models / Ionic liquids

Data availability

Experimental, predicted, and input data used to build the intelligent framework models are accessible from Brunel University London repository at: doi: 10.17633/rd.brunel.23937918.v1.

Credit author statement

Behnaz Basirat: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing -original draft, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing -review & editing. Fariborz Shaahmadi: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing -original draft, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing -review & editing. Seyed Sorosh Mirfasihi: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing -original draft, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing -review & editing. Abolfazl Jomekian: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing -original draft, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing -review & editing. Bahamin Bazooyar: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing -original draft, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing -review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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