Gas Solubility Research Articles

Molecular Dynamics Simulations of Gas Transport Properties in Cross-Linked Polyamide Membranes: Tracing the Morphology and Addition of Silicate Nanotubes.

This study employs molecular dynamics (MD) simulations to fundamentally provide insight into the role of cross-link density in the CO2 separation properties of interfacially polymerized polyamide (PA) membranes. For this purpose, two atomistic models of pure polyamide membranes with different cross-link densities are constructed by MD simulations to conceptually determine how the fractional free volume of polyamide affects the gas separation performance of the membrane. The PA membrane with a lower cross-link density (LCPA) shows a higher gas diffusion coefficient, a lower gas solubility coefficient, and a higher gas permeability than the PA membrane with a higher cross-link density (HCPA). Moreover, the pristine and modified silicate nanotubes (SNTs) as the fast gas transport channels are incorporated into the polyamide membranes to assess the effect of the SNT/PA interface chemistry on the CO2 separation properties of the membranes. SNTs are systematically modified by three modifying agents with different CO2-philic groups and different interfacial interaction energies with the polyamide matrix. The results of MD simulations demonstrate that the incorporation of silicate nanotubes into the PA matrix increases the gas diffusivity and permeability and decreases the CO2/gas selectivity. Moreover, the membranes containing modified SNTs possessing high CO2-philicity and high SNTs/PA interfacial interactions show a high CO2 separation performance.

Investigating temperature-dependent gas permeation in PIM-1: Hysteresis, annealing effects, and thermal history impact

This study investigates the gas permeability, diffusion coefficients, and solubility coefficients of CO2 and N2 in PIM-1 over the temperature range of 35 °C–135 °C. During the first heating-cooling cycle, both gases' permeability exhibited hysteresis, which diminished during the second cycle. Thermal mechanical analysis confirmed volume relaxation occurring at temperatures above 95 °C, indicating physical aging. By calculating the relaxation time associated with volume relaxation, the influence of physical aging was minimized through measurements on samples annealed at 135 °C for 15 h. This approach enabled a successful evaluation of the temperature dependence of gas permeability under conditions where the impact of physical aging could be disregarded. The results revealed that the permeability of CO2 decreased, and that of N2 increased with increasing temperature. The diffusion coefficients showed positive temperature dependence, while the solubility coefficients decreased. Physical aging significantly affected the diffusion coefficients but had minimal impact on the solubility coefficients. Heat of sorption and activation energies for gas permeability and diffusion estimated, and it was found that the decrease in CO2 permeability with temperature was mainly attributed to the large heat of sorption for CO2.

Modeling hydrogeological conditions of noble gases in water and aqueous electrolyte solutions

Dissolved noble gases in water hold significant potential as tracers and precursors in various hydrogeological and geological applications, particularly in understanding surface water-groundwater interactions. These applications necessitate precise solubility predictions. Employing the fugacity-fugacity approach, our study models the solubilities of Neon, Argon, Krypton, Xenon, and Radon in both pure water and electrolyte solutions. Leveraging the Cubic-Plus-Association Equation of State, we achieve remarkable accuracy, with average deviations below 2.5 % for vapor pressure, liquid, and vapor-phase densities. Additionally, in water–gas binary systems, deviations in gas solubilities within pure water remain under 5.0 %. Extending our analysis to water–gas-salt ternary systems, our model demonstrates deviations smaller than 5.6 %. Alongside discussing the gas dissolution mechanism, this work emphasizes the adaptability of the model, informed by robust experimental and modeling data. The interpretations derived from modeling hydrogeological conditions are poised to enhance our understanding of hydrological processes, thereby assisting planners and water managers in achieving sustainable development and management of water resources.

Pattern classification of bearing faults in PMSM based on time domain feature ensembles

This research aims to identify an effective feature-based pattern classification technique that uses vibration and current data to identify bearing conditions. The authors attempted non-conventional time-domain features to detect the bearing conditions in permanent magnet synchronous motors (PMSM). This work employs two case studies utilizing eight datasets from Paderborn University to identify the bearing conditions of three and twelve classes. In this work, support vector machine, k-nearest neighbor, random forest, decision tree, and naive Bayes classifiers with 10% holdout validation are applied to study 31 feature combinations. This study also examines the Henry gas solubility optimization technique for feature selection to identify the most discriminating features. The results indicate that four feature ensembles consisting of 2 to 5 features performed better with the support vector machine, k-nearest neighbor, and random forest classifiers. In contrast to previous relevant studies, the proposed features are useful in identifying PMSM-bearing conditions with the highest accuracy of 99.8% and 99% using current signals for 3 and 12 classes respectively for combined current operating conditions.

High thermodynamical sensitivity of CO2 emissions from a large oligotrophic-hardwater lake (Nam Co) on the Tibetan Plateau

The Tibetan Plateau (TP) has the world's largest distribution of high-alpine and saline (generally hardwater) lakes, which are expected to affect regional carbon cycling profoundly. However, the variability, and especially underlying factors controlling CO2 dynamics, across widespread hardwater lakes is poorly understood on the TP. Here, we present year-round records of surface water pCO2 from a representative hardwater lake (Nam Co) on the TP, and analyze relationships between ambient variables and pCO2 during open water (i.e., ice-free) and ice-covered months. Surface pCO2 (233.3 μatm on average) was a little oversaturated to atmosphere (219 μatm on average) during the open water season. As a CO2 source, Nam Co emitted 8.73 ± 1.06 Gg C annually, but this flux only accounted for 0.53 ± 0.06 ‰ of its total dissolved inorganic carbon pool (1.64 × 1013 g C). Regression results indicate that, during open water months, both seasonal and diurnal varying patterns of surface pCO2 were influenced predominantly by water temperature, in a quasi-marine mode, by controlling gas solubility and dissolved carbonate equilibria. Therefore, CO2 evasion was elevated during summer months, despite the lake being autotrophic (i.e., CO2 consumption via photosynthesis). By contrast, during ice-covered months the surface pCO2 was strongly related to under-ice thermodynamics, and declined nonlinear with increased inversed stratification. In the hypolimnion, as a result of extremely weak metabolism (as indicated by low dissolved oxygen depletion rates) and a combined high carbonate buffering effect, accumulation of CO2 was negligible, leading to an absence of peak effluxes of CO2 during turnover periods, compared to eutrophic freshwater lakes. We argue that, under future global warming scenarios, consideration of the impact of gradually warming lake water on thermodynamics and dissolved carbonate equilibria are vital in order to understand the future CO2 dynamics of these widespread high-altitude oligotrophic-hardwater lakes situated across the TP.

Using Porous Liquids to Perform Liquid-Liquid Separations.

Porous liquids (PLs) are a new type of fluid sorbent investigated mainly for the separation of gas mixtures. Here, we explore their application to the separation of miscible liquids, using MEG/water (MEG=monoethylene glycol) and EtOH/water as proof-of-principle. Recovery of used MEG is industrially important but its extraction into conventional solvents from water is difficult. PLs ZIF-8@PDMS (PL1, PDMS=polydimethylsilicone) or ZIF-8@sesame oil (PL2) each consisting of 25 wt % of the hydrophobic microporous material ZIF-8 dispersed in PDMS or sesame oil respectively, were formulated and found to be exceedingly physically stable to sedimentation. A 5 nm PEEK membrane was used to provide a permeable barrier between the PL and the alcohol/water phase. MEG was selectively extracted through the membrane from approximately 50 : 50 wt % MEG/water mixtures into the PL phase and this procedure could be applied repeatedly. It was effective for MEG/water mixtures as dilute as 3 : 97 wt %. The PL could also be regenerated (80 °C at 0.01 bar) and re-used, suggesting its potential for continuous, cyclic extraction. Furthermore, PL3 (silicalite-1@PDMS) was effective in selective alcohol extraction from beverages. It shows potential for lowering the alcohol concentration in gin or wine due to its excellent chemical stability and nontoxicity. Overall, we show that the enhanced adsorption properties of PLs due the presence of empty pores,which provides unusually high gas solubilities, also makes them, in principle, applicable to liquid-liquid separations.

Using Porous Liquids to Perform Liquid‐Liquid Separations

Porous liquids are new types of fluid sorbent investigated mainly for the separation of gas mixtures. Here, we explore their application to the separation of miscible liquids, MEG (monoethylene glycol)/water and EtOH/water) as proof of principle. Recovery of used MEG is industrially important but its extraction from water is difficult. PLs ZIF 8@PDMS (PL1, PDMS = polydimethylsilicone) or ZIF‐8@sesame oil (PL2) each consisting of 25wt% of the hydrophobic microporous material ZIF‐8 dispersed in PDMS/sesame oil, were formulated and found to be exceedingly physically stable. 5 nm PEEK membranes were used to provide permeable barriers between the PL and the alcohol/water phase. MEG was selectively extracted through the membrane from 50wt% MEG/water mixtures into the PL phase. It was effective for MEG/water mixtures as dilute as 3:97wt%. The PL could be regenerated and re‐used, suggesting its potential for continuous cyclic extraction. Furthermore, PL3 (silicalite‐1@PDMS) has demonstrated effectiveness in achieving selective alcohol extraction from beverages. It shows great potential for lowering the alcohol concentration in gin/wine due to its excellent chemical stability and nontoxicity. Overall, the enhanced adsorption properties of PLs due the presence of empty pores, which provides unusually high gas solubilities, also makes them, in principle, applicable to liquid‐liquid separations.

Electric-field nanobubbles for agriculture

Electric-field nanobubbles for agriculture Niall J. English, from Chemical Engineering at University College Dublin, discusses how using electric-field-generated nanobubbles for agriculture is empowering fundamental progress. A fundamental challenge in crop and tillage agriculture lies in providing adequate levels of dissolved oxygen (DO) in the water supply whether via irrigation or spray-delivery methods for respective root-zone or stomatal delivery, with also CO2 in the latter case. This is limited by Henry’s Law, which governs thermodynamic gas solubility in liquids.

Influence of semi-crystalline microstructure on gas permeability of Poly(Ether-Ketone-Ketone)

In a context of ecological transition, aeronautics is moving towards the development of hydrogen-powered aircraft for which lightweight, long-lasting and tough storage tanks must be developed. Among other properties, the tightness of tanks made of polymer and composite materials is of prime importance. This study thus aims at understanding the permeation phenomena through Poly-Ether-Ketone-Ketone (PEKK), which is one of the thermoplastic polymers considered for tank manufacture. In order to better understand the phenomena governing gas permeability, tests were carried out on different PEKK grades of different T/I ratios and degree of crystallinity. The results show that the crystallinity ratio is not the only factor that governs gas diffusion and solubility, but that permeability also depends on PEKK semi-crystalline microstructure. Even if considering crystallites organization through a tortuosity factor improves permeability predictions, permeability can be better understood using a 3 phases description of PEKK microstructure considering distinct contributions of the rigid and mobile amorphous fractions (RAF and MAF). It appears that the MAF permeation does not depend on PEKK T/I ratio, but that gas diffusion and solubility increase for RAF when increasing T/I ratio, thus counterbalancing the blocking effect of crystallites.

A comparative study on the solubility of CH3SH and H2S in imidazolium-based ionic liquids: A connectionist approach between free volume and thermodynamics

Sulfur contaminants in industrial gas streams, such as hydrogen sulfide and mercaptans, must be eliminated due to safety and environmental considerations. Ionic liquids, as a new class of chemical compounds, are an alternative to conventional aqueous alkanolamine solutions currently used in absorption gas treatment processes. In this work, the solubilities of methyl mercaptan (CH3SH) and hydrogen sulfide gases in 1-butyl-3-methylimidazolium-based ionic liquids with tetrafluoroborate ([C4mim][BF4]), hexafluorophosphate ([C4mim][PF6]), trifluoromethanesulfonate ([C4mim][OTf]), and bis(trifluoromethylsulfonyl)imide ([C4mim][Tf2N]) anions have been studied using molecular dynamics (MD) simulation and a recently developed OPLS all-atom (OPLS-AA-0.8) force field. A new concept, the induced free volume, has been introduced in this work, which provides a realistic and precise connection between the thermodynamic entropic contributions to the dissolution process of CH3SH and H2S in the ionic liquids, on one hand, and the microscopic free/void volume induced as a result of the entrance of a gas solute molecule into the liquid structure, on the other hand. The thermodynamic energetic contributions were correlated to the CH3SH/H2S interaction with the ionic species of the ionic liquids. Quantum chemical energy decomposition analysis (EDA) was employed to calculate and analyze the binding interaction and nature of CH3SH/H2S interactions with the ionic species of ionic liquids. It was found that the van der Waals contributions to the interaction of CH3SH with ionic liquids are more significant compared to that of H2S, while the opposite conclusion could be deduced for the corresponding electrostatic contributions. Both CH3SH and H2S established equivalent hydrogen bonding interactions with the ionic liquids studied in this work. The diffusion coefficients of the ionic species, as well as the excess chemical potentials and Henry’s constants for the dissolution of CH3SH and H2S in the ionic liquids, were calculated by free energy perturbation technique (FEP) and compared with the MD simulated and experimental data, reported in the literature. It was concluded that the OPLS-AA-0.8 force field yields Henry’s constants and diffusivities that are in better agreement with the reliable experimental data corresponding to other OPLS versions reported to date.

Credit card fraud detection using the brown bear optimization algorithm

Fraud detection in banking systems is crucial for financial stability, customer protection, reputation management, and regulatory compliance. Machine Learning (ML) is vital in improving data analysis, real-time fraud detection, and developing fraud techniques by learning from data and adjusting detection strategies accordingly. Feature Selection (FS) is essential for enhancing fraud detection through ML to achieve optimal model accuracy. This is because it helps to eliminate the negative impact of redundant and irrelevant attributes. To enhance the accuracy of the given dataset, the researchers utilized multiple methods to determine the most fitting features. However, it is important to note that when implementing these methods on datasets with larger feature sizes, they may encounter issues with local optimality. Despite this, the researchers continue to work on improving the effectiveness of these methods. This study presents an effective methodology based on the Brown-Bear Optimization (BBO) algorithm to enhance the capacity to accurately identify financial CCF transactions by recognizing pertinent features. BBO has balanced capabilities to reduce dimensionality while enhancing classification accuracy. It is improved by adjusting the positions randomly to enhance exploration and exploitation capabilities, and then it is cloned into a binary variant named Binary BBOA (BBBOA). The Support Vector Machine (SVM), k-nearest Neighbor (k-NN), and Xgb-tree are the ML classifiers used with the suggested methodology. On the Australian credit dataset, the proposed methodology is compared with the basic BBOA and ten current optimizers, such as Binary African Vultures Optimization (BAVO), Binary Salp Swarm Algorithm (BSSA), Binary Atom Search Optimization (BASO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Sailfish Optimizer (BSFO). Regarding Wilcoxon’s rank-sum test (α=0.05), the superiority and effective consequence of the presented methodology are clear on the utilized dataset and got an accuracy of classification up to 91% in the utilized dataset combined with an attribute reduction length down to 67%. The proposed methodology is further validated using 10 benchmark datasets and outperformed its competitors in most utilized datasets regarding different performance measures. In the end, the proposed methodology is further validated using ten benchmark datasets from the UCI repository. It outperformed its competitors in most of the utilized datasets regarding different performance measures.

HYBRID OPTIMIZATION-ENABLED FUNCTIONAL CONNECTIVITY-BASED PIVOTAL REGION EXTRACTION AND TRANSFER LEARNING FOR AUTISM SPECTRUM DISORDER DETECTION

Autism Spectrum Disorder (ASD) is a neuro development-based disability caused by variations in the brain which occur during the early stages of human life. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. However, diagnosing ASD is difficult due to its complex symptoms as well as an inadequate number of neurobiological indications. This paper aims to detect ASD based on pivotal region extraction and Feedback-Henry Jellyfish Optimization-Convolution Neural Network with Transfer Learning (FHJO_CNN with TL) is developed in this work. Here, an autistic brain image is acquired from the data sample and it is fed to the pre-processing stage. By utilizing the Kalman filter and Region of Interest (RoI), selection pre-processing is done. After that, functional connectivity-based pivotal region extraction is performed based on the proposed FHJO, where the FHJO is an incorporation of Feedback-Henry Gas Optimization (FHGO) and Jelly Fish (JS) algorithms, where, FHGO is the combination of Henry Gas Solubility Optimization (HGSO), and Feedback Artificial Tree (FAT) algorithm. Thereafter in feature extraction, features like Local Binary Pattern (LBP), and Speeded Up Robust Features (SURF), statistical features are excerpted. Finally, ASD classification is done utilizing the proposed FHJO_CNN with TL. The performance of the proposed method is evaluated using the ACERTA-ABIDE dataset and the evaluation reveals that FHJO_CNN with TL attained values of accuracy, sensitivity and specificity which are 94.08%, 95.09%, and 93.59% respectively.

Analysis of the effect of inert gas on alveolar/venous blood partial pressure by using the operator splitting method.

This study aims to investigate how inert gas affects the partial pressure of alveolar and venous blood using a fast and accurate operator splitting method (OSM). Unlike previous complex methods, such as the finite element method (FEM), OSM effectively separates governing equations into smaller sub-problems, facilitating a better understanding of inert gas transport and exchange between blood capillaries and surrounding tissue. The governing equations were discretized with a fully implicit finite difference method (FDM), which enables the use of larger time steps. The model employed partial differential equations, considering convection-diffusion in blood and only diffusion in tissue. The study explores the impact of initial arterial pressure, breathing frequency, blood flow velocity, solubility, and diffusivity on the partial pressure of inert gas in blood and tissue. Additionally, the effects of anesthetic inert gas and oxygen on venous blood partial pressure were analyzed. Simulation results demonstrate that the high solubility and diffusivity of anesthetic inert gas lead to its prolonged presence in blood and tissue, resulting in lower partial pressure in venous blood. These findings enhance our understanding of inert gas interaction with alveolar/venous blood, with potential implications for medical diagnostics and therapies.

The thermal equation of state of xenon: Implications for noble gas incorporation in serpentine minerals and their transport to depth

Isotopic signatures of heavy noble gases in the Earth's mantle contain a major component recycled by subduction. The experimental and field studies reported in the literature show increasing evidence that serpentine minerals can hold large quantities of noble gases, potentially serving as their primary vectors to depth. However, at present, their retention mechanism in these minerals is not fully understood. Additionally, noble gas solubilities from field and experimental studies show large differences in terms of elemental concentrations. Here, we performed crystal chemical modeling to evaluate the incorporation mechanism of noble gases and their solubilities in serpentine minerals along subduction zone geotherms. To this end, we determined the thermal equation of state of xenon using in situ X-ray diffraction and absorption up to 60 GPa and 728 K. In this range, the xenon equation of state is well-adjusted using the Mie-Grüneisen-Debye formalism with relevant fitting parameters. We show that the experimentally observed solubility trend, which follows the order Ne < He < Ar < Kr < Xe, can be explained by the incorporation of noble gases at two distinct crystallographic sites. The light noble gases He and Ne are most likely retained at the van der Waals hydrogen-oxygen bond position between the layers, while the heavy and larger noble gases enter the voids between the six-membered SiO4 rings. It should be noted that octahedral sites can potentially host xenon, but cannot accommodate argon and krypton. Indeed, this would require unrealistic flexibility of the crystal lattice. Our models extended to mantle wedge conditions predict decreasing solubilities, particularly for light noble gases, in agreement with observations from natural samples. Compared to the noble gas concentrations determined experimentally in serpentine, natural concentrations are much higher and very variable. Our solubility model confirms that equilibrium processes cannot explain these observations. We therefore suggest that the high and variable noble gas concentrations found in natural samples must be due to hybrid hydration processes in ultramafic rocks that involve different degrees of water activities.

Understanding noble gas incorporation in mantle minerals: an atomistic study

Ab initio calculations in forsterite (Mg2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document}SiO4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_4$$\\end{document}) are used to gain insight into the formation of point defects and incorporation of noble gases. We calculate the enthalpies of incorporation both at pre-existing vacancies in symmetrically non-equivalent sites, and at interstitial positions. At high pressure, most structural changes affect the MgO6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{6}$$\\end{document} units and the enthalpies of point defects increase, with those involving Mg and Si vacancies increasing more than those involving O sites. At 15 GPa Si vacancies and Mg interstitials have become the predominant intrinsic defects. We use these calculated enthalpies to estimate the total uptake of noble gases into the bulk crystal as a function of temperature and pressure both in the presence and absence of other heterovalent trace elements. For He and Ne our calculated solubilities point to atoms occupying mainly interstitial sites in agreement with previous experimental work. In contrast, Ar most likely substitutes for Mg due to its larger size and the deformation it causes within the crystal. Incorporation energies, as well as atomic distances suggest that the incorporation mainly depend on the size mismatch between host and guest atoms. Polarization effects arising from the polarizability of the noble gas atom or the presence of charged defects are minimal and do not contribute significantly to the uptake. Finally, the discrepancies between our results and recent experiments suggest that there are other incorporation mechanisms such as adsorption at internal and external interfaces, voids and grain boundaries which must play a major role in noble gas storage and solubility.

Advanced machine learning computations for estimation of hydrogen solubility in oil samples: Model comparisons and validation

For analyzing hydrogenation process for treatment of petroleum-based fuels, solubility of hydrogen in the feed should be well correlated. The main correlation factors are temperature and pressure which have a great effect on hydrogen solubility. This research paper presents the development of three models for predicting the solubility of hydrogen gas (H2) in diesel. Pressure and Temperature are the input parameters and solubility is single output. The models were fine-tuned using the Bat Algorithm (BA). The three models include Orthogonal Matching Pursuit Regression (OMP), K Nearest Neighbors Regression (KNN), and Tweedie Regression (TDR). The results of the study revealed that the OMP model achieved the highest level of accuracy, with an R2 score of 0.98, and the least RMSE and MAE error rates of 0.24 and 0.19, respectively. The KNN model also performed well with an R2 score of 0.92, an RMSE of 0.42, and an MAE of 0.37. The TDR model had the lowest accuracy compared to the other two models. These results imply that the OMP model is the most suitable one for predicting H2 solubility. The models can be used to enhance the efficiency of fuel production by providing accurate predictions of H2 solubility.

Bulk Nanobubbles through Gas Supersaturation Originated by Hot and Cold Solvent Mixing.

The nucleation mechanism of bulk nanobubbles remains unclear despite the considerable attention they have received in recent years. We propose two hypotheses: (i) The gas supersaturation in the bulk liquid is the primary factor for nanobubble nucleation, and (ii) the mixing of the same solvent at varying gas solubilities should produce nanobubbles, provided that the first hypothesis is correct. To test this hypothesis, we performed extensive experiments on nanobubble nucleation in both water and organic solvents. The temperature difference between hot and cold samples ranged from 10 to 80 °C in pure solvents such as water, methanol, ethanol, propanol, and butanol prepared and mixed in equal proportions. To the best of our knowledge, we report bulk nanobubble nucleation by mixing hot and cold solvents for the first time. The refractive index value calculations using Mie scattering theory confirmed the existence of nanobubbles. When surface tension dominates over surface charge, the critical work for nanobubble formation is ΔFc ∝ 1/ξ2, and when surface charge dominates over surface tension, the critical work is ΔFc ∝ ξ1/4. Our experimental results verify such dependency by measuring nanobubbles nucleated with varying degrees of gas supersaturation.

Solubility of H2S-CH4 mixtures in calcium chloride solution: Insight from molecular dynamics simulations

This study uses equilibrium molecular dynamics (EMD) simulations to gain microstructural insight into and predict crucial properties of gas-brine mixtures affecting sour gas reservoir development. The effects of pressure, temperature, gas composition and salinity on the solubility and density of H2S-CH4 mixtures in pure water and CaCl2 brine were investigated at industrially relevant conditions (temperature range of 280 − 340 K, pressures up to 20 MPa, and a salinity range of 4.78 − 17.32 wt%). The force fields used in the MD simulations were validated using experimental data for pure H2S and CH4. The results showed that the solubility of pure H2S and CH4 in pure water increases with pressure, with the latter having a higher solubility. For H2S-CH4 mixtures, the solubility of H2S increased while that of CH4 fell with increasing H2S mole fraction, with the solubilities of the two gases intersecting at an H2S mole fraction of 0.28. Further, the solubility of H2S shows a greater pressure dependence than that of CH4, and H2S gas tends to reach saturation in pure water. In calcium chloride solution, the gas solubility decreases as the ions reduce the interaction between water and gas molecules. The solubility of H2S gas is a stronger function of the ion concentration compared with CH4, and increases 6-fold as the H2S mole fraction increases from 0.5 to 0.8. The overall solubility ranges were 0.45 to 3.48 mol/kg for H2S and 0.15 to 0.25 mol/kg for CH4.

Semi-empirical model for Henry’s law constant of noble gases in molten salts

Henry’s law constant, which describes the proportionality of dissolved gas to partial pressure of free gas in liquid–gas equilibrium systems, can also be applied to mass transport applications. In this work, we investigated an approach for determining the solubility of noble gases in a molten salt liquid utilizing the equilibrium concept of Henry’s gas constant. Henry’s gas constant is described as a mathematical function dependent on the van der Waals radius of the noble gas and the temperature of the molten salt. The alteration in Gibbs free energy encompasses contributions from both surface and volume energies. Enthalpy and entropy are deduced from these surface and volume energies in the Gibbs free energy formulation. A comparative analysis was conducted between the conventional method and our proposed model. Moreover, useful chemical properties can be determined from examination of surface and volume energies. Our findings provide an accurate and general theory of Gibbs free energy that can be validated experimentally based on the model proposed herein. This work unifies the prediction of Henry gas constant and subsequently the entropy and enthalpy calculation for noble gases in a molten salt solution to a single functional form using van der Waals radius of the gas and temperature of the system. This functional form is then used to perform a multiple regression method to find two parameters corresponding to the surface energy and volume energy. These two parameters are consistent between all combinations of noble gas and molten salt.

CNNFDNF: CONVOLUTIONAL NEURAL NETWORK AND CASCADE NEURO FUZZY NETWORK FOR SEVERITY LEVEL OF COVID-19 DETECTION USING CHEST X-RAY IMAGES

Chest X-ray (CXR) images are the most important imaging modality to monitor and detect several lung diseases and Coronavirus Disease 2019 (COVID-19) disease. This is the first imaging method in the detection of COVID-19. Since the limited availability of interpreted medical images, and the accuracy are the biggest challenges in the conventional methods. Hence, a novel framework is designed for the severity level of COVID-19 detection using CXR images named Convolutional Neural Network and Cascade Neuro-Fuzzy Network (CNNFDNF). Initially, the input CXR image is allowed to the preprocessing phase that is done utilizing the Kalman filter and Region of Interest (ROI) extraction. Then, the lung lobe segmentation is executed by Psi-Net. After that, the extraction of Convolutional Neural Network (CNN) features is conducted from the interested lobe region. Moreover, the extracted image features are subjected to COVID-19 detection, where it is achieved by Deep Generative Adversarial Network (Deep GAN) trained by Henry Gas Water Wave Optimization (HGWWO). Here, HGWWO is blended by Water Wave Optimization (WWO) and Henry Gas Solubility Optimization (HGSO). The detection phase is categorized into COVID and non-COVID. If the detected image is COVID, then the CXR images are given to the second level classification. Finally, it is accomplished by employing the proposed CNNFDNF to classify them into low, moderate, or high COVID-19 infection affected at lungs. The proposed CNNFDNF is the newly devised scheme by modifying the layers of CNN and cascaded neuro-fuzzy network (DNF) structure. The measures employed for this scheme namely, accuracy, sensitivity and specificity acquired are 93.0%, 94.1%, and 93.8%. The proposed method is essential for keeping the situation under control. Because, in the severe cases may have severe pneumonia, other organ failure, and possible death.