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

Modelling of Wax Deposition by Perturbed Hard Sphere Chain Equation of State

This article presents a model to predict the wax appearance temperature (WAT) and the quantity of wax deposition in eight different n-alkane mixtures using a correlative technique. The perturbed hard sphere chain equation of state (PHSC EoS) was employed in conjunction with the multi-solid model to describe the liquid-liquid and solid-liquid equilibria. The results are compared with experimental data. The results showed that PHSC EoS for some mixture of n-alkanes can perceptibly outperform the sole solid solution theory, improving the modelling of wax deposition quantities and wax appearance temperature by giving predictions closer to experimental values

Separation of CO2 from CH4 by pure PSF and PSF/PVP blend membranes : effects of type of nonsolvent, solvent, and PVP concentration

Complete CO2/CH4 gas separation was aimed in this study. Accordingly, asymmetric neat polysulfone (PSF) and PSF/polyvinylpyrrolidone (PVP) blend membranes were prepared by wet/wet phase inversion technique. The effects of two different variables such as type of external nonsolvent and type of solvent on morphology and gas separation ability of neat PSF membranes were examined. Moreover, the influence of PVP concentration on structure, thermal properties, and gas separation properties of PSF/PVP blend membrane were tested. The SEM results presented the variation in membrane morphology in different membrane preparation conditions. Atomic forced microscopic images displayed that surface roughness parameters increased significantly in higher PVP loading and then gas separation properties of membrane improved. Thermal gravimetric analysis confirms higher thermal stability of membrane in higher PVP loading. Differential scanning calorimetric results prove miscibility and compatibility of PSF and PVP in the blend membrane. The permeation results indicate that, the CO2 permeance through prepared PSF membrane reached the maximum (275 ± 1 GPU) using 1-methyl-2-pyrrolidone as a solvent and butanol (BuOH) as an external nonsolvent. While, a higher CO2/CH4 selectivity (5.75 ± 0.1) was obtained using N-N-dimethyl-acetamide (DMAc) as a solvent and propanol (PrOH) as an external nonsolvent. The obtained results show that PSF/PVP blend membrane containing 10 wt % of PVP was able to separate CO2 from CH4 completely up to three bar as feed pressure

Synthesis of ultra-porous zeolitic imidazolate Framework-300 under different synthesis conditions for sorption of CO2

Data availability: Data will be made available on request.We examined here how different ratios of precursors, temperature, time, and synthesis environment affected the crystal structure, texture, and CO2/N2 sorption behavior of the ZIF-300 samples synthesized. We found that a higher bmim/meIm molar ratio resulted in larger particle size, increased pore volume, and higher BET surface area due to a more crystalline structure and well-shaped pore structure. This was attributed to a decrease in the pH of the synthesis solution caused by the higher bmim/meIm molar ratio, which lowered the ζ potential and reduced electrostatic repulsion between particles, leading to the formation of larger particles with a more crystalline structure. Increasing the temperature of the synthesis solution resulted in smaller particles due to a decrease in supersaturation levels and the formation of smaller nuclei. The duration of synthesis had a positive effect on particle size as both growth and aggregation of smaller particles occurred over time. NMP and DMA were found to be unsuitable synthesis environments as they produced large particles with poor gas sorption performance. In terms of texture, ZIF-300 samples synthesized with a higher bmim/meIm molar ratio and at lower temperatures exhibited higher BET surface area and pore volume compared to other samples. Additionally, samples synthesized with a molar ratio of bmim/meIm = 4/1 and 2/1 showed better selectivity for CO2/N2 solubility compared to similar cases reported in previous studies.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors except from Esfarayen University of Technology

The Experimental, Technical, and Economical Evaluations of Green Fabricated Activated Carbon/PVA Mixed Matrix Membrane for Enhanced CO<inf>2</inf>/CH<inf>4</inf> Separation

In the experimental part of the work, carbon powders were synthesized and then activated in this work from tea and carbonated soft drink (coke) wastes. The synthesized activated carbon (AC) particles were incorporated into the matrix of PVA to form mixed matrix membranes (MMMs) to be examined for the separation of CO2 from CH4. SEM, FTIR, N2 adsorption-desorption, DFT pore size distribution (PSD), and BET analysis were used for the characterization of MMMs. The applied permeation tests indicated that tea wastes were better precursors for the synthesis of ACs than coke for the removal of carbon dioxide from methane by AC/PVA MMMs. In the technical and economical evaluation of the work, MATLAB programming was used. The result of the techno-economical evaluation of membranes in a designed two-stage membrane separation configuration showed the maximum process global performance index (IGP) of 351.9 for a sample and it appeared that the CO2 recovery was the most effective factor in IGP determination. The turbine for generating power was determined to be only economically interesting in the process with the lowest gas flow rates. The membrane lifetime and the plant lifetime were determined to be two of the most important parameters affecting the total costs of the system. Membranes with an average lifetime below 6 months and the plant lifetime below 20 years, cause a considerable rise in total costs of the system and potential economical failure of the designed system configuration.This research did not utilize any source of grants, except for the Esfarayen University of Technology faculty grant

Carbon capture via aqueous ionic liquids intelligent modelling

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-mean-squared-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

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

The role of heat recirculation and flame stabilization in the formation of NOX in a thermo-photovoltaic micro-combustor step wall

The health and durability of micro thermophotovoltaic systems are contingent upon the level of gaseous emissions of micro combustors regarding their small size, thickness, and compactness. In small combustion devices, the flame stabilization is achieved via conjugated heat transfer from the stabilized flame to the fresh reactant via the step of the micro-combustors. The step could also create a recirculation of products, and a stagnation zone for the fluid, as a result leading to the accumulation of pollutants. In turbulent H2 flame, the main attention is given to the NOX as no other noxious emission, especially carbon emission (CO, CO2, PAH, and VOC), form during the combustion of hydrogen. The existence of NOX in the presence of water, as in the combustion of hydrogen is prevalent, could lead to corrosion in combustor interior walls and other detrimental impacts for the ecosystem. In the presented work, micro-combustion of H2 flame in a cylinder with a step is simulated and the formation of nitrogen oxides is analyzed. The influence of different combustor specifications (equivalence ratio, solid materials) NOX species are discussed and evaluated. Results revealed nitrogen oxides form and accumulate in the vertical step of the microchannel and that the microchannel walls are more prone to the high concentrations of nitrogen oxides. The application of cavity promotes the two-dimensionality of flow, resulting in effective heat transfer from the hot gas to the cavity walls. This not only leads to flame anchoring to the cavity walls but also results in significant NOX