Property estimation of water/alcohol/ionic liquid ternary system: Density

One of the most highly important applications of ionic liquids is for separation of the components of water/alcohol azeotropic mixture. The use of ionic liquids for this purpose creates a ternary system for which the determination of thermodynamic behavior and physical properties is highly matter of interest in design and operation of effective separation plants. In this work, the density of the aqueous ionic liquid ternary system is modelled based on three intelligent connectionist approaches. For the modelling, 1663 experimental density data points for ternary systems including 17 different ionic liquids in four aqueous alcohols (methanol, ethanol, propanol, and pentanol) solution were analyzed and taken into account at temperature range [288–343] K and pressure ranges [100–3000] kPa. The Shuffled complex evolution (SCE) algorithm was employed for optimization of the model parameters and constants. Four statistical parameters of R2, AARD, RMSE and STD were calculated for the connectionist models to compare their performance in the degree of comprehensiveness and accuracy. The highly matched results of the modelling with experimental demonstrate that the intelligent approach provides a suitable replacement for conventional thermodynamic models and equation of states which need too many fitting parameters

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

Research data for this article: Data not available / The authors do not have permission to share data.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0920410519310782?via%3Dihub#appsec2 .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

Modeling of negative Poisson’s ratio (auxetic) crystalline cellulose Iβ

Energy minimizations for unstretched and stretched cellulose models using an all-atom empirical force field (Molecular Mechanics) have been performed to investigate the mechanism for auxetic (negative Poisson’s ratio) response in crystalline cellulose Iβ from kraft cooked Norway spruce. An initial investigation to identify an appropriate force field led to a study of the structure and elastic constants from models employing the CVFF force field. Negative values of on-axis Poisson’s ratios nu31 and nu13 in the x1-x3 plane containing the chain direction (x3) were realized in energy minimizations employing a stress perpendicular to the hydrogen-bonded cellobiose sheets to simulate swelling in this direction due to the kraft cooking process. Energy minimizations of structural evolution due to stretching along the x3 chain direction of the ‘swollen’ (kraft cooked) model identified chain rotation about the chain axis combined with inextensible secondary bonds as the most likely mechanism for auxetic response

Validating empirical force fields for molecular-level simulation of cellulose dissolution

The calculations presented here, which include dynamics simulations using molecular mechanics force fields and first principles studies, indicate that the COMPASS force field is preferred over the Dreiding and Universal force fields for studying dissolution of large cellulose structures. The validity of these force fields was assessed by comparing structures and energies of cellobiose, which is the shortest cellulose chain, obtained from the force fields with those obtained from MP2 and DFT methods. In agreement with the first principles methods, COMPASS is the only force field of the three studied here that favors the anti form of cellobiose in the vacuum. This force field was also used to compare changes in energies when hydrating cellobiose with 1-4 water molecules. Although the COMPASS force field does not yield the change from anti to syn minimum energy structure when hydrating with h more than two water molecules - as predicted by OFT - it does predict that the syn conformer is preferred when simulating cellobiose in bulk liquid water and at temperatures relevant to cellulose dissolution. This indicates that the COMPASS force field yields valid structures of cellulose under these conditions. Simulations based on the COMPASS force field show that, due to entropic effects, the syn form of cellobiose is energetically preferred at elevated temperature, both in vacuum and in bulk water. This is also in agreement with DFT calculations

Continuum - Molecular Modeling of Graphene Lattice

In the present contribution we address the modeling of graphene membranes - the thinnest membrane structure man ever has produced. Due to the covalent bond configuration of the Carbon, the nano-membranes are predicted to have promising electrical as well as mechanical properties;resonators, force/mass sensors and nanoswitches are some examples of the future graphene\u27s applications.A hierarchy of modeling approaches are investigated in order to assess the proper scale bridging strategy with respect to graphene membrane structures. Accurate models, such as Ab-Initio (AI) and Density Function Theory (DFT), are exploited and compared to a first order homogenized, higher scale Molecular Dynamics (MD) approach for a set of planar unit lattices. The lower scale AI, DFT and MD-models are conveniently used to model the behavior of small to medium size lattices,whereas the extension to large scale lattices and membrane structures becomes overly computationally demanding

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

Continuum - Molecular Modeling of Graphene Lattice

In the present contribution we address the modeling of graphene membranes - the thinnest membrane structure man ever has produced. Due to the covalent bond configuration of the Carbon, the nano-membranes are predicted to have promising electrical as well as mechanical properties;resonators, force/mass sensors and nanoswitches are some examples of the future graphene's applications.A hierarchy of modeling approaches are investigated in order to assess the proper scale bridging strategy with respect to graphene membrane structures. Accurate models, such as Ab-Initio (AI) and Density Function Theory (DFT), are exploited and compared to a first order homogenized, higher scale Molecular Dynamics (MD) approach for a set of planar unit lattices. The lower scale AI, DFT and MD-models are conveniently used to model the behavior of small to medium size lattices,whereas the extension to large scale lattices and membrane structures becomes overly computationally demanding