Carbon capture via aqueous ionic liquids intelligent modelling

Data availability: Experimental, predicted, and input data used to build the intelligent framework models are accessible from Brunel University London repository at: https://doi.org/10.17633/rd.brunel.23908371.v1.Copyright © 2023 The Author(s).. With conventional thermodynamic models, it is challenging to estimate the solubility of a gas in the presence of impurities such as water (H2O). Intelligent models can be utilised for this goal in a computationally efficient manner. In this paper, the carbon dioxide (CO2) solubility in ionic liquids (ILs) containing water is predicted using three intelligence models: artificial neural network (ANN), support vector machines (SVM), and least square support vector machine (LSSVM). The shuffled complex evolution (SCE) is used to optimise the intelligent models SVM and LSSVM hyperparameters (σ2 and γ), whereas trial and error are used to determine the optimum numbers of neurons and layers for the ANN. To identify the most efficient model, the capabilities of applied intelligent models for determining solubility were compared. The findings show agreement between the experimental values and model estimations. Given that the coefficient-of-determination (R2) and root-meansquared-error (RMSE) were found to be, respectively, 0.9965 and 0.0104 for the test data points, ANN is shown to be moderately more accurate than SVMs or LSSVM at predicting solubility. It can also be inferred that from a statistical point of view, when fed with parameters such as R2, RMSE, standard deviation (STD), and average-absolute-percentage-deviation (AARD), the ANN model demonstrated superior precision in predicting gas solubilities compared to the SVM and LSSVM models

Modeling and simulation of hydrocarbon dew point adjustment of natural gas via supersonic separators

This chapter provides a thorough analysis of the supersonic gas separation technique for the removal of heavy hydrocarbons and water from natural gas streams. It examines the underlying concepts of supersonic gas separation, the benefits and limitations of the technology, as well as the current modeling and simulation tools. In addition, a technoeconomic study of the supersonic gas separation process is offered, which includes a full review of the process economics, cost drivers, and prospective optimization areas. The assessment underlines the promising performance of the supersonic gas separation technology, but also suggests areas for additional research and optimization, notably in the context of enhancing the technoeconomic viability of the process

Intelligent Solubility estimation of Gaseous Hydrocarbons in Ionic Liquids

Peer review under responsibility of Southwest Petroleum University.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 is 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 models, 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 like ML-ANN, SVM, and LSSVM for 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.TBC © 2023 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd