Average Absolute Relative Deviation Research Articles

Assessment of abiotic reduction rates of organic compounds by interpretable structural factors and experimental conditions in anoxic water environments

For organic contaminants in lake sediments, aquifers, and anaerobic bioreactors, their reduction is one of the primary transformation paths in these anoxic water environments. A simple model is introduced to predict pseudo-first order rate constants (kobs) for the abiotic reduction of organic compounds featuring diverse reducible functional groups. It utilizes the largest experimental dataset of –log kobs, encompassing 59 organic compounds (278 data points). Unlike available complex quantitative structure–activity relationship (QSAR) methods, the novel approach requires both experimental conditions and structural parameters. In comparison to one of the available general QSAR methods, the new model demonstrates favorable performance. The average absolute deviation (AAD), absolute maximum deviation (ADmax), average absolute relative deviation (AARD%), and R-squared (R2) values of the estimated outputs for 54/5 training/test data sets of the new model are 0.641/1.761, 1.761/1.417, 20.52/83.87, and 0.797/0.949, respectively. On the other hand, the available general comparative QSAR method shows the AAD: 1.311/2.301, ADmax: 3.795/3.732, AARD%: 641.0/821.2, and R2: 0.003/0.447. For the test set, AAD, AARD%, ADmax, and R2 values for the new/comparative models are 0.649/2.403, 62.20/190.5, 1.215/3.732 and 0.974/0.789, respectively. In summary, the new model offers a straightforward approach for the manual calculation of –log kobs, demonstrating excellent goodness-of-fit, reliability, precision, and accuracy.

New insight and experimental study of ineffective void in hydrodynamic performance of countercurrent‐flow packing column

AbstractExtensive experimental research and hydrodynamic models have been proposed to guide the design of superior packings. However, most research has concentrated on the effective void (ε − H) of packing while ignoring the ineffective void (ε − H − HL), which causes discrepancies in hydrodynamic performance compared to actual observations. This study evaluated the hydrodynamic performance under diverse conditions considering the liquid holdup (H), pressure drop (ΔP), and gas flooding velocity (uf). A novel approach to hydrodynamic model construction is introduced by incorporating an ineffective void. The results indicate that at a constant hold‐up area, liquid flow (L) and viscosity (μ) significantly influence liquid hold up, moderated by the gas velocity in the flooding area. The pressure drop rises as the viscosity, gas flow rate, and liquid flow rate increase. Notably, a considerable pressure drop initiates flooding at the bottom of the absorber. Elevated liquid flow rates and viscosities correlate with higher ineffective void values (HL) in the packing column. At low gas flow rates, the gas flow rate marginally affects HL values. However, after the flooding point was achieved, the values of HL rapidly increased as the gas flow rate increased. Moreover, a linear relationship emerges between the liquid holdup and HL, as evidenced by the consistent variation in the liquid holdup and the F‐factor. Utilizing the ineffective void yields a more accurate fit for the experimental data, reducing the average absolute relative deviation to 10.2%, 7.4%, and 10.8%, respectively.

Modeling self‐diffusion coefficients of ionic liquids using ePC‐SAFT and FVT combined with Einstein relation

AbstractThe electrolyte perturbed‐chain statistical associating fluids theory (ePC‐SAFT) coupled with free volume theory (FVT) was combined with Einstein relation, that is, ePC‐SAFT‐FVT‐E, to describe self‐diffusion coefficients (SDCs) of ionic liquids (ILs). In modeling, ePC‐SAFT was used to calculate density, while FVT parameters, determined from viscosity data, were utilized to calculate the summation of ionic SDCs through the Einstein relation with one parameter. Two strategies were employed to determine this parameter: fitting experimental data for each IL or estimating a universal parameter from van der Waals volume. Comparative analysis reveals good agreement with experimental data, with average absolute relative deviations (ARDs) of 8.14% (strategy 1) and 10.29% (strategy 2). Subsequently, cationic and anionic SDCs were reliably determined from the summation of ionic SDCs, with average ARDs of 10.80% and 10.21%, respectively. This study indicates the ePC‐SAFT‐FVT‐E model, employing viscosity‐derived parameters and three universal parameters, reliably predicts SDCs of ILs in wide temperature and pressure ranges.

Dynamic viscosity of low GWP refrigerants in the liquid phase: An empirical equation and an artificial neural network

This study presents a simple correlation for describing the temperature and pressure dependence of the liquid dynamic viscosity of low GWP refrigerants, namely HydroFluoroOlefins (HFOs) and HydroChloroFluoroOlefins (HCFOs). The model has 3 input parameters (i.e., reduced temperature, reduced pressure, and acentric factor) and 6 coefficients which were regressed on 794 experimental data collated from the literature for 7 alternative refrigerants (i.e., R1233zd(E), R1234yf, R1234ze(E), R1234ze(Z), R1224yd(Z), R1336mzz(E), and R1336mzz(Z)). Moreover, a multi-layer perceptron neural network for the liquid dynamic viscosity of the studied fluids was developed from the selected dataset. The artificial network has the same 3 input parameters of the correlation and one hidden layer with 19 neurons. The results of the proposed correlation proved that it is an accurate model for calculating the dynamic viscosity of the studied liquid refrigerants, despite its simplicity. It ensured an average absolute relative deviation of the liquid dynamic viscosity (AARD(η)) of 2.88 %, lower than that given by other literature correlations. As expected, the multi-layer perceptron neural network provided the best results for all the selected refrigerants (AARD(η) = 0.86 % for the complete dataset), proving that it can be considered a reference for the development of other models.

Solubility prediction of refrigerants in PEC lubricants based on back-propagation neural network combined with genetic algorithm

In the present study, a backpropagation neural network combined with genetic algorithm (GA-BP) model was constructed for prediction the solubility of refrigerants in linear chained precursors of POE lubricants (PECs). A total of 2248 experimental solubility data of refrigerants in PECs reported in literature were collected with temperatures from 243.15 K to 363.15 K and pressures up to 10 MPa. The input variables of the model were optimized using non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The optimized inputs include temperature, pressure, molecular weight, critical temperature, and acentric factor. Results indicate that the GA-BP model using the optimized inputs can correlate the solubility data with good accuracy, the average absolute relative deviation between calculated results from the model and the literature is 0.98 %. Moreover, in order to validate the predictive ability of the established GA-BP model, the solubility of R1243zf in PEC4 and PEC5 was measured at the temperature range from 278.15 K to 343.15 K. The calculated values from the GA-BP model were compared with the experimental data, and the average absolute relative deviation is 8.85 %. Finally, sensitivity analysis was performed by Partial Derivatives (PaD) method to assess the contribution of input variables. Leverage approach was used for outlier detection to ensure the robustness of the model.

Open access, thermodynamically consistent, electrolyte NRTL model for piperazine, AMP, water, CO2 systems on Aspen Plus

This study developed a comprehensive, open-access, thermodynamically consistent, validated vapour-liquid equilibrium model for CO2 absorption into aqueous solutions of piperazine (PZ) activated 2-amino-2-methyl-1-propanol (AMP) on Aspen Plus. This solves a two-decade-old problem of inconsistent specification of PZ, and lacking AMP/PZ, thermodynamic models on Aspen Plus, supporting robust process modelling of AMP/PZ-based CO2 capture systems, considered the contemporary benchmark. The model coverage is wide: total amine concentrations from 30 wt% to 50 wt%, AMP/PZ mole ratios from 0.46 to 23.1, CO2 loadings from 0.04 to 1.07 mol CO2/mol amine, and temperatures from 20 °C to 160 °C. The significance lies in the model’s innovative treatment of the zwitterion PZH+CO2–, leading to accurate VLE predictions and closed charge balances. Absolute average relative deviations (AARD) are 16.6 % to 22.3 % for CO2 partial pressure predictions and 6.3 % to 7.7 % for absorption heats. Gibbs-Helmholz and flash calculated absorption heats consistently compare within 1.5 % to 7.0 %, signalling the model’s thermodynamic consistency.

Predicting the solubility of solids in supercritical carbon dioxide using the Chrastil equation with parameters estimated from a group contribution method

Predicting the solubility of a solid in supercritical carbon dioxide (scCO2) can be useful for many applications. However, an accurate prediction is extremely difficult since current methods require knowledge about the properties of the solid in question or lengthy computational calculations and often yield significant deviations. Here, a new approach is proposed that allows the solubility of any solid in supercritical carbon dioxide to be rapidly predicted based solely on the structural formula of the solute. This approach is based on the prediction of Chrastil’s parameters using group contribution methods (GCMs) followed by their use in Chrastil’s equation. For this purpose, group contribution methods to estimate Chrastil’s parameters were developed using more than 6000 experimental data points from 210 solids. These parameters were used to predict the solubility data of 30 solids (around 1300 experimental points) in pure scCO2 with Chrastil’s equation, obtaining a relatively accurate fit for 70 % of the experimental points. The average absolute relative deviation was 51.22 % (less than 8.31 · 10−4 in absolute deviation), while the logarithmic deviation was 10.83 %. The deviations obtained were substantially lower than other estimations proposed in the literature. This methodology is easily extended, faster, and more accurate than other methods and does not require the use of computational methods or the estimation of solid properties. Moreover, new ways to predict the solubility of solids in supercritical carbon dioxide can be developed in the future based on this methodology.

Experimental (ρ,P,T) data of H2 + CH4 mixtures at temperatures from 278 to 398 K and pressures up to 56 MPa

The blending of hydrogen and natural gas (H2-NG) has emerged as a crucial and cost-effective means for processing hydrogen fluids through natural gas networks thereby aiding transition to net-zero energy. Nonetheless, the effective adaptation of H2-NG mixtures, especially up to 20% hydrogen concentrations, to existing natural gas distribution and storage facilities relies heavily on a comprehensive understanding of their thermophysical properties. We measured densities of three binary mixtures of hydrogen and methane (xH2 = 0.0532, 0.0858, and 0.2031) at temperatures between 278 and 398 K, and pressures up to 56 MPa using a vibrating tube densimeter (VTD). The VTD was calibrated using H2 and H2O. The measured densities were in reasonable agreements with the predictions of two thermodynamic equations of states (EoSs), Multi-Fluid Helmholtz Energy Approximation (MFHEA) and Peng-Robinson (PR). The average absolute relative deviation (AARD) for the measured density against the predictions of the PR EoS are 1.07%, 1.09%, and 0.89% for xH2 = 0.0532, 0.0858, and 0.2031 mol fractions respectively. While the AARD for the measured density against the predictions of MFHEA EoS are 0.16%, 0.16%, and 0.32% for xH2 = 0.0532, 0.0858, and 0.2031 mol fractions respective. The MFHEA EoS, which has the structure of GERG-2008, has better agreements with our density data than the PR EoS. The relative high deviations with PR EoS results from the PR's simple structure making it deficient in calculating thermal properties of natural gas in saturated-liquid and homogeneous states. In addition, the uncertainty of the measured density was determined, and the experimental data were compared with available literature data. Finally, the compressibility factor (Z), speed of sound (w), specific heat capacity at constant volume (CV), and specific heat capacity at constant pressure (Cp) were obtained from the experimental results and were compared with the predictions of MFHEA EoS. This work provides accurate density and other thermophysical data useful for engineering/process designs and modelling of H2 and natural gas systems thereby enhancing energy transition strategy.

Prediction of diffusion coefficients in aqueous systems by machine learning models

Currently there are no accurate models for the prediction of diffusion coefficients at infinite dilution in aqueous systems. Frequently, models that work well for polar solvents often perform worse in the case of water. At the same time, experimental data of tracer diffusion coefficients are scarce and can be impractical to measure when information on this important transport property is required. In this work, machine learning models were developed to predict the tracer diffusion coefficient of any solute in water at atmospheric pressure. Several approaches were carried out to construct the model, using different types of input parameters: pure component properties and theoretical molecular descriptors, such as atom counts, structural fragments and fingerprints, computed using different sources. A database of 126 systems (1192 data points) was used for training and the best model showed a global average absolute relative deviation (AARD) of 3.92%, with a maximum deviation of 24.27% on the test set. This model uses as inputs the temperature and 195 molecular descriptors computed using the RDKit cheminformatics package, which can be automatically calculated from a molecular identifier thus making the model very simple to use. In comparison, the well-known Wilke-Chang equation provided an AARD of 13.03% in the same test set, demonstrating the improved accuracy of the proposed solution. The models developed in this work are provided at github.com/EgiChem/ml-D12-water-app.

Application of four intelligent models for the estimation of formation pressure of natural gas hydrate

Natural gas hydrate is a clean and efficient resource that contains lots of heat energy. The production, processing and transportation of natural gas can be influenced dramatically by natural gas hydrate. Natural gas hydrate will accumulate at the bottom of the pipeline, even block the pipeline and reduce the flow rate of the natural gas. This article presents four intelligent models named GWO-SVM (Grey Wolf Optimizer connected with Support Vector Machine), GWO-LSSVM (Grey Wolf Optimizer connected with Least Squares Support Vector Machine), GA-LSSVM (Genetic Algorithm connected with Least Squares Support Vector Machine), and GWO-RBF (Grey Wolf Optimizer connected with Radial Basis Function) to predict the formation pressure of natural gas hydrate in different systems. In order to determine the degree of influence of each input variable on the formation pressure of natural gas hydrate, this paper uses Spearman correlation analysis method to calculate the correlation coefficients between the formation pressure of natural gas hydrate and various influencing factors. The contradistinction among different intelligent models shows that the GWO-LSSVM model has the exceptional performance of the smallest average absolute relative deviation (AARD=2.89%), the minimum standard deviation (SD=0.05) with all 244 data of natural gas hydrate formation pressure in different systems. When compared to six commonly used empirical models for predicting formation pressure in common natural gas hydrate, the GWO-LSSVM model has the highest prediction accuracy, with an AARD of only 1.82%. Furthermore, when compared to three thermodynamic models employed for predicting formation pressure in sour natural gas hydrate, the GWO-LSSVM model has the highest prediction accuracy, with an AARD of 2.38%. The leverage method demonstrates that four intelligent models have excellent robustness in reflecting the inner relationships among natural gas hydrate formation pressure and influential factors. The four intelligent models for predicting hydrate formation pressure will be helpful to find hydrate formation pressure in different systems.

Solubility measurement of methane in triethylene glycol aqueous solutions (90,95,98 wt.% triethylene glycol) from 298 to 323 K and 0.17 to 2.5 MPa

Limited phase equilibria data exists for the methane + water + triethylene glycol system. In this work, new phase equilibrium data were measured for this system for a temperature range of 298 to 323 K and a pressure range of 0.17 to 2.5 MPa. These data add to the information that is required in the design of TEG contactor units since they normally operate over a temperature and pressure range of 283 to 329 K, and 2 to 10 MPa respectively. The data were measured using a static synthetic apparatus. The measured data were modelled in Apen Plus V12.1 using the Cubic Plus Association equation of state. The model correlation yielded a satisfactory description of the measured data since the maximum absolute average relative deviation in pressure was 4.30 %.

Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents.

Carbon dioxide (CO2) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO2 capture. Chemically reactive DESs, which form chemical bonds with the CO2, are superior to nonreactive, physically based DESs for CO2 absorption. However, there are no accurate computational models that provide accurate predictions of the CO2 solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO2 in chemically reactive DESs. As training data, we collected 214 data points for the CO2 solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO2 solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO2 solubility precisely and thus accelerate the design and application of chemically reactive DESs.

Modeling of ionic liquids viscosity via advanced white-box machine learning.

Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9MPa) and temperatures (253.15-573K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDHmodel with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.

Influence of water on the properties of hydrophobic deep eutectic solvent

Deep eutectic solvent (DES) shows excellent absorption performance in the field of CO2 capture. It is considered as a new green absorption solvent to replace monoethanolamine. In the industry, the presence of water not only affects the structural stability of the solvent, but also affects its thermophysical properties. In this work, three kinds of hydrophobic DES were prepared. Based on the study of their structural characteristics and thermal stability, the density and viscosity of DES with water mixture were measured in the temperature range of 298.15 ∼ 338.15 K at atmospheric pressure. It was demonstrated that the trace water did not destroy the structure of DES, but it formed hydrogen bonds with DES. The increase water destroyed the structure of DES and formed hydrogen bonds with hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA), respectively. Moreover, the hard sphere model was applied to study the viscosity, and after introducing the binary interaction parameter, the average absolute relative deviation of the mixture reduced to 4.4 ∼ 7.7 %.

Developing statistical and machine learning models for predicting CO2 solubility in live crude oils

CO2 solubility in crude oil primarily affects the viscosity and swelling capacity of oil and determines the displacement efficiency of CO2 flooding during the enhanced oil recovery process. Dissolution of CO2 into crude oil also ensures safe and permanent storage of CO2 in depleted formations via solubility trapping. This study employed statistical and machine learning algorithms to develop precise and stable predictive models for estimating CO2 solubility in crude oils with dissolved gas. Recognizing the limitations of existing experimental data on CO2 solubility for live oils (crude oils with dissolved gas), we introduced a novel approach employing statistical and machine learning techniques for developing accurate and robust predictive models. Our methodology combined regularization, k-fold cross-validation, and statistical significance tests to ensure the robustness of the models, practices not considered in the previous studies. A linear regression model with L2 regularization is developed as the base model and compared with a 2nd order polynomial model and an ensemble feed-forward neural network (FNN) model integrated with gradient boosting (GB). The incorporation of GB into the neural network capitalizes on the additive sequence of FNN models, thereby enhancing the accuracy and reliability of predictions. Moreover, an improved user-accessible and easily implementable correlation is proposed to estimate CO2 solubility in live crude oils at diverse operating conditions as a function of reservoir temperature, saturation pressure, bubble-point pressure, oil molecular weight, and specific gravity. The performances of the developed model and the correlation are compared against the literature models and correlations. The FNN model with an overall R2 score of 0.9978, average absolute relative deviation (AARD) of 1.54 %, and root mean squared error (RMSE) of 0.0083 outperformed the previously published models and correlations. The new correlation, while being relatively simpler than previously published correlations, showed similar predictive performance with an overall R2 score of 0.9817, AARD of 4.98 %, and RMSE of 0.0225. The model and the correlation can be used to estimate CO2 solubility in live crude oils with 0.67 to 0.97 specific gravity for reservoir temperatures and pressures in the range of 28 to 124 °C and 3.23 to 32.76 MPa, respectively.

New insights into estimating the cementation exponent of the tight and deep carbonate pore systems via rigorous numerical strategies

AbstractOne of the main constituents of any reservoir characterization is an accurate forecast of water saturation, which is highly dependent upon the cementation exponent. Even though there have been a lot of studies, the most common correlations depend on total porosity. This means that they do not work as well in heterogeneous carbonate reservoirs, especially tight formations with total porosities less than 10%. This study aims to develop accurate and universal models for estimating the cementation exponent in deep and tight carbonate pore systems located in West Asia. Two heuristic algorithms, including the radial basis function neural network optimized by ant colony optimization (RBFNN-ACO) and gene expression programming (GEP), were employed to calculate the cementation exponent. To do this, we prepared a databank incorporating cementation exponents, total porosity, and various pore types. Then, the databank is classified into the test subset (for model prediction checking) and the train subset (for model construction). The reliability of the new recommended models is inspected by applying several statistical quality measures associated with graphical analyses. So, the consequences of the modeling disclose the large precision of the above-mentioned RBFNN-ACO, GEP Model-I, and GEP Model-II by average absolute percentage relative deviations (AAPRD%) of 6.28%, 6.39%, and 7.45%, respectively. Based on the outliers analysis, nearly 95% of the databank and model estimations are, respectively, valid and reliable. Additionally, the three input variables, including moldic porosity (with a + 70% impact value), non-fabric-selective dissolution (connected) porosity (with a -30% impact value), and interparticle porosity (with a -23% impact value), exhibit the main affecting parameters on the cementation exponent. Comparing current results with traditional literature correlations demonstrates the supremacy of the RBFNN-ACO model (AAPRD = 6.28% and root mean squared error (RMSE) = 0.17) over the examined literature correlations such as Borai’s equation (AAPRD = 12.30% and RMSE = 0.41). In addition, RBFNN-ACO can give better results than Borai’s Eqn. for tight (porosity less than 10%) and deep carbonate samples.

ASCD: Automatic sensing and control device for crop irrigation scheduling

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.

Determination of Regorafenib monohydrate (colorectal anticancer drug) solubility in supercritical CO2: Experimental and thermodynamic modeling

In this study, the solubilities of Regorafenib monohydrate (REG), a widely used as a colorectal anticancer drug, in supercritical carbon dioxide (ScCO2) were measured under various pressures and temperature conditions, for the first time. The minimum value of REG in mole fraction was determined to be 3.06×10−7, while the maximum value was found to be 6.44×10−6 at 338 K and 27 MPa. The experimental data for REG were correlated through the utilization of two types of models: (1) a set of 25 existing empirical and semi-empirical models that incorporated 3–8 parameters according to functional dependencies, (2) a model that relied on solid-liquid equilibrium (SLE) and the newly improved association models. All of the evaluated models were capable of generating suitable fits to the solubility data of REG, however, the average absolute relative deviation (AARD) of Gordillo et al. model (AARD=13.2%) and Reddy et al. model (AARD=13.5%) indicated their superiority based on AARD%. Furthermore, solvation and sublimation enthalpies of REG drug were estimated for the first time.

Liquid-Liquid equilibrium measurements analysis of synthetic multicomponent solvent (n-Pentane, n-Hexane, n-Heptane, Cyclo-Hexane, Toluene)/Bitumen System: Implications for solvent-aided in-situ bitumen extraction

Understanding the Liquid-Liquid Equilibrium (LLE) of multicomponent solvent/bitumen systems is pivotal in optimizing in-situ bitumen extraction. While Vapor-Liquid Equilibria (VLE) of solvent-bitumen systems has been extensively studied, LLE data and predictive models for these mixtures, especially those involving synthetic multicomponent solvents, are scarce. This study provides comprehensive LLE measurements of bitumen and a multicomponent synthetic solvent (n-pentane + n-hexane + n-heptane + cyclo-hexane + toluene) conducted under controlled equilibrium conditions at temperatures of 295 to 352 K, solvent concentrations up to 70 wt% at 1.291 MPa. Application of common correlation used in the construction of solvent-aided process fluid models exhibited average absolute relative deviations (AARDs) of 0.37 % for density and 8.87 % for viscosity. We identified the boundaries marking the shift from homogenous single-phase liquid to two-phase (LLE) states, which occurs within the synthetic multicomponent solvent concentration range of 50 to 60 wt%. We also successfully constructed calibration curves to measure solvent concentrations in both phase regions. The molecular weight distribution and saturates, aromatics, resins, and asphaltene (SARA) fractionation of both liquid phases were determined, highlighting that the heavy liquid phase is primarily asphaltene (∼46 wt%). The insights gained from the measurements and analysis find applications in designing and optimizing solvent-assisted thermal recovery processes for bitumen extraction.

An optimal approach of pure/mixed CO2 sequestration with producing hydrated natural gas: Physical insights and validation

In this contribution, an optimal approach is developed to sequestrate pure/mixed CO2 by dislodging the hydrated natural gas, the cleanest fossil fuel, thereby catching two birds by one stone. For depicting its intricacies, a transient thermo-kinetic swapping model is developed with considering first time the effect of permeability and water saturation, along with porosity involved in sediments of hydrate reservoir. The formulation takes into account various fundamental issues having practical relevance, like porous particles with uneven size and shape, surface dynamics involved in phase transformation, non-unity reaction order (i.e., nth order), among others. A mixed-integer nonlinear multi-objective sequential optimization (MOSO) problem is articulated within the framework of a robust global optimizer (i.e., nondominated sorting genetic algorithm II) to deal with two conflicting objectives, concerning minimization of absolute average relative deviation and maximization of swapping efficiency. This optimal approach is validated with laboratory and field data, showing its outperformance over the latest models at diverse geological conditions. The proposed model framework is employed for lab-scale cases to identify the optimal guest gas (CO2/N2) ratio that secures the maximum swapping efficiency. Simulation results suggest that the presented theoretical model is rigorous in interpreting these swapping dynamics in hydrates.