The use of surfactants for oil and gas purposes has been explored and extensively applied. However, such systems' thermodynamic and predictability behavior still needs further investigation. This work aims to describe the phase behavior of the systems composed of nonionic ethoxylated surfactants (nonylphenyl ethoxylated surfactants—IGEPAL CO-630 and CO-720) + carbon dioxide (CO2) with additional methanol in wide ranges of temperature (T = 313 to 413 K), pressure (P = 0.8 to 54.8 MPa), and CO2 molar compositions (up to 90.0 %). It was observed for all systems studied that CO2 solubility was inversely proportional to temperature and the number of ethoxylated units for pure surfactants. The addition of methanol greatly increases the overall CO2 solubility. The thermodynamic modeling through the PC-SAFT equation of state was also evaluated. Bubble pressure deviations of 13.63 % and 13.49 % were obtained for pure IGEPAL CO-630 and IGEPAL CO-720 + CO2, respectively, and 12.63 % for the ternary system (IGEPAL CO-720 + methanol + CO2). A new set of binary interaction parameters was also provided for IGEPAL CO-630 and IGEPAL CO-720 + CO2.

The aim was to gain a deep understanding of the research status and application of machine learning in the field of ionic liquids, and to identify the research hotspots and frontiers. Co-occurrence analysis, co-citation analysis, key literature citation temporal analysis and emerging word analysis were used. The results show that the knowledge base of applying machine learning in the field of ionic liquids can be divided into three parts: fundamental properties and application research, thermodynamics and phase equilibrium research, and the combination of machine learning with computational chemistry methods. The research hotspots mainly include the prediction and optimization of properties, the prediction of phase behavior, and research on machine learning algorithms. The current research frontiers in applying machine learning in the field of ionic liquids include the prediction of ionic liquid performance, the structure-property relationships of ionic liquids, and the optimization and design of ionic liquid processes.

Although there are several group contribution methods available for predicting thermodynamic properties, the uncertainties associated with these predictions are unknown. In this study, we present a new group contribution method approach based on bayesian neural networks (BNNs) in order to provide predictions and their associated uncertainty. Various machine learning techniques, including a fully connected neural network (FCNN) and a graph neural network (GNN) under the BNNs framework, were employed to efficiently implement this methodology. As a case of study, we focused on predicting the melting and boiling points of cannabinoids and terpenes, as well as their corresponding uncertainties. Individual analyses were carried out for boiling and melting points using databases containing 2529 and 2862 chemical compounds, respectively. Additionally, we conducted an assemble study of both properties using a database of 1503 chemical compounds. To validate the models, a database consisting of 47 cannabinoids and terpenes was employed. The models exhibited exceptional prediction results for boiling points, showcasing coefficients of determination R2≥ 0.9 for all the studied models. On the contrary, only the assemble GNN model yielded accurate melting point predictions with an R2 value of 0.94, while the R2 values obtained from the other models ranged from 0.51 to 0.66. Finally, our predictions for the melting and boiling points of two well-known cannabinoids, CBD and THC, are (70.5±45.4) °C and (419.4±25.4) °C for CBD, and (87.0±46.7) °C and (411.8±29.7) °C for THC, respectively.

The study presents a comprehensive evaluation of ten distinct methods for solving cubic equations, encompassing both analytical, numerical, and linear algebra-based approaches. Among these, an analytical technique involving the transformation of cubic equations into Chebyshev polynomials emerged as the most efficient. To mitigate issues arising from numerical round-off errors, the study proposes an iterative refinement strategy. Furthermore, the research introduces a novel criterion grounded in Vieta's formulas to identify and isolate areas where round-off errors may occur. This criterion aids in the application of the proposed iterative refinement technique. The novel solution method, which involves the conversion of cubic equations into Chebyshev polynomials and subsequent error-checking and iterative refinement, exhibits significant advantages. Firstly, it proves efficient, reducing computational time by 15–20 % in comparison to the well-established Cardano analytical method. It also demonstrates robustness in effortlessly identifying regions of potential error and offers a method for iterative improvement when needed. Additionally, its reliability is underscored by its independence from the specific cubic equation of state under consideration, making it well-suited for the intensive solution of a wide range of cubic equations.

As water consumption increases due to industrialization and global population growth, climate change and urbanization exacerbate the limited available freshwater resources. To counter the challenge water desalination has been used to provide potable water that is ready for human consumption. However, most of the existing water desalination techniques have persistent drawbacks despite decades of development. Thus, a need for efficient and sustainable techniques is urgent. One of the emerging water desalination techniques is hydrate-based desalination. While clathrate has been a nuisance in the oil and gas industry, it has been investigated in several applications including water desalination. This work investigates thermodynamic effectiveness of two hydrate formers in the presence of chloride salts. Methane or carbon dioxide with Tetra n‑butyl ammonium bromide have been used as formers. While the presence of salts depresses the pressure effect on semiclathrate equilibrium on the tested range, it does not have a clear effect at low TBAB concentration. Thus, based on this study using a low concentration of TBAB (20 wt%) with low to moderate pressure of carbon dioxide would yield the most favorable thermodynamic conditions for hydrate-based desalination.

This work explores the thermophysical properties of the SPC/E model of water at temperatures between 250 and 400 K and at pressure from 0.1 to 1000 MPa. From a series of Molecular Dynamics simulations (in the NPT ensemble and also in the NVT ensemble), the constant pressure and constant volume heat capacity, the isobaric expansion coefficient, the isothermal compressibility, and the speed of sound are obtained from fluctuation formulas and thermodynamic numerical derivatives and compared with available experimental data.

In this study, we aimed to elucidate the molecular interactions in deep eutectic solvents (DESs) and acquire additional experimental evidence to increase our understanding of the molecular mechanisms responsible for the preparation of DES. We investigated three N-methylthiourea (NMTU)-based DESs with choline chloride (ChCl), allyltrimethylammonium chloride (ATMAC), and benzyltriethylammonium chloride (BTEAC) as hydrogen bond acceptors (HBA). To accomplish this, we used both experimental techniques including isopiestic, differential scanning calorimetry (DSC), and FTIR measurements as well as molecular dynamics (MD) simulations. The thermal behaviors of the prepared DESs were investigated by DSC method. In order to study the interaction and hydrogen bonding between the DESs constituents, FT-IR analysis and isopiestic measurements were carried out. Constant solvent activity lines of ternary HBA + HBD + solvent mixtures were determined by the isopiestic technique. The large positive deviation of the isosolvent activity lines from the semi-ideal behavior indicates a strong interaction between HBA and HBD, which is much stronger than HBA-HBA and HBD-HBD interactions. Classical MD simulations were performed at 298.15 K to analyze the nanoscopic properties and structure of the DESs. To investigate the interaction between the components and visualize the three-dimensional structure of the DESs, radial distribution functions (RDFs), coordination numbers (CNs), and combined (CDFs), and spatial (SDFs) distribution functions were determined by MD simulations. The obtained theoretical results confirmed the importance of hydrogen bonds in the preparation of DESs and also showed that these systems exhibit different structural arrangements. MD simulations were also used to determine the density of the DESs and the results showed good agreement with the experimental density.

To achieve high computational efficiency and simulation accuracy in the equation-of-state-based compositional simulation, the fast multiphase flash calculation approach is required for the hydrocarbon-CO2 system, allowing for the simultaneous existence of vapour, CO2-lean, and CO2-rich phases. However, the conventional step-by-step method for multiphase flash calculation is associated with a high cost in identifying the three-phase equilibrium, thereby significantly impeding the computation efficiency of compositional simulation when the three-phase equilibrium appears in most grids.To address this challenge, a novel approach to multiphase flash calculation is proposed. In this study, all trial phases exhibiting negative tangent plane distance are employed to identify the three-phase equilibrium, eliminating the need for additional phase stability analysis and two-phase split calculations. Furthermore, this new method incurs minimal computational cost and requires only minor modifications to the existing stepwise procedure. Phase diagram calculations conducted on four cases demonstrate that the newly proposed method dramatically accelerates the identification of the three-phase equilibrium.

Equations of state (EoS) are powerful tools for estimating a wide variety of physical properties. However, the applicability of parameter sets derived from specific physical properties for the correlation and estimation of various other physical properties has received limited attention. Additionally, the estimation of physical properties using EoS is anticipated to be affected by the association of molecules. The densities of homogeneous phase fluid mixtures comprising carbon dioxide (CO2)/methanol (MeOH) and CO2/ethanol (EtOH) binary systems were measured in the current study using a high-pressure vibration-type density meter equipped with a circulation pump and a variable-volume viewing cell. Homogeneity was ensured by observing the fluid through the viewing window of the variable volume cell. The measurements were carried out at a temperature range of 313–353 K, the CO2 mole-fraction range of 0–80 mol%, and at pressures up to 20 MPa. Subsequently, the as-obtained experimental data were correlated with two EoSs, viz. Sanchez-Lacombe (SL) EoS and Perturbed Chain statistical associating fluid theory (PC-SAFT) EoS. The density correlations between SL and PC-SAFT EoS were almost identical in accuracy. Additionally, the association between CO2 and alcohols in PC-SAFT EoS had no discernible effect on the reliability of the density correlations. The vapor liquid equilibria (VLE) of the CO2/MeOH and CO2/EtOH mixtures were further estimated using parameter sets determined from the density measurements. Both the EoSs demonstrated comparable estimation accuracy; however, the pressure was estimated primarily near the critical region of the mixture, which yielded a lower estimation accuracy. Additionally, the densities of the binary systems were determined using characteristic parameters derived from the VLE correlations. Of the EoSs, the PC-SAFT EoS yielded a good correlation of the VLE, including the region near the mixture's critical region, while taking the association between CO2 and alcohols into consideration. Although few of the correlations were observed to be inferior, the density of the homogeneous fluid mixture was accurately estimated using the two EoSs, with the parameters obtained from the VLE correlations. The findings of the study thus suggest that in order to estimate the density and VLE using EoS-shared parameters, the parameter sets must first be determined using a VLE that exhibits a wide range of conditions affected by the system's associations.