Application of machine learning techniques to the modeling of solubility of sugar alcohols in ionic liquids.

The current trend of chemical industries demands green processing, in particular with employing natural substances such as sugar-derived compounds. This matter has encouraged academic and industrial sections to seek new alternatives for extracting these materials. Ionic liquids (ILs) are currently paving the way for efficient extraction processes. To this end, accurate estimation of solubility data is of great importance. This study relies on machine learning methods for modeling the solubility data of sugar alcohols (SAs) in ILs. An initial relevancy analysis approved that the SA-IL equilibrium governs by the temperature, density and molecular weight of ILs, as well as the molecular weight, fusion temperature, and fusion enthalpy of SAs. Also, temperature and fusion temperature have the strongest influence on the SAs solubility in ILs. The performance of artificial neural networks (ANNs), least-squares support vector regression (LSSVR), and adaptive neuro-fuzzy inference systems (ANFIS) to predict SA solubility in ILs were compared utilizing a large databank (647 data points of 19 SAs and 21 ILs). Among the investigated models, ANFIS offered the best accuracy with an average absolute relative deviation (AARD%) of 7.43% and a coefficient of determination (R2) of 0.98359. The best performance of the ANFIS model was obtained with a cluster center radius of 0.435 when trained with 85% of the databank. Further analyses of the ANFIS model based on the leverage method revealed that this model is reliable enough due to its high level of coverage and wide range of applicability. Accordingly, this model can be effectively utilized in modeling the solubilities of SAs in ILs.

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios.

High oil prices and concern about limited oil reserves lead to increase interest in enhanced oil recovery (EOR). Selecting the most efficient development plan is of high interest to optimize economic cost. Hence, the main objective of this study is to construct a novel deep-learning classifier to select the best EOR method based on the reservoir's rock and fluid properties (depth, porosity, permeability, gravity, viscosity), and temperature. Our deep learning-based classifier consists of a one-dimensional (1D) convolutional neural network, long short-term memory (LSTM), and densely connected neural network layers. The genetic algorithm has been applied to tune the hyperparameters of this hybrid classifier. The proposed classifier is developed and tested using 735 EOR projects on sandstone, unconsolidated sandstone, carbonate, and conglomerate reservoirs in more than 17 countries. Both the numerical and graphical investigations approve that the structure-tuned deep learning classifier is a reliable tool to screen the EOR scenarios and select the best one. The designed model correctly classifies training, validation, and testing examples with an accuracy of 96.82%, 84.31%, and 82.61%, respectively. It means that only 30 out of 735 available EOR projects are incorrectly identified by the proposed deep learning classifier. The model also demonstrates a small categorical cross-entropy of 0.1548 for the classification of the involved enhanced oil recovery techniques. Such a powerful classifier is required to select the most suitable EOR candidate for a given oil reservoir with limited field information.

Application of Green Polymeric Nanocomposites for Enhanced Oil Recovery by Spontaneous Imbibition from Carbonate Reservoirs.

This study experimentally investigates the effect of green polymeric nanoparticles on the interfacial tension (IFT) and wettability of carbonate reservoirs to effectively change the enhanced oil recovery (EOR) parameters. This experimental study compares the performance of xanthan/magnetite/SiO2 nanocomposites (NC) and several green materials, i.e., eucalyptus plant nanocomposites (ENC) and walnut shell ones (WNC) on the oil recovery with performing series of spontaneous imbibition tests. Scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDAX), and BET (Brunauer, Emmett, and Teller) surface analysis tests are also applied to monitor the morphology and crystalline structure of NC, ENC, and WNC. Then, the IFT and contact angle (CA) were measured in the presence of these materials under various reservoir conditions and solvent salinities. It was found that both ENC and WNC nanocomposites decreased CA and IFT, but ENC performed better than WNC under different salinities, namely, seawater (SW), double diluted salted (2 SW), ten times diluted seawater (10 SW), formation water (FW), and distilled water (DIW), which were applied at 70 °C, 2000 psi, and 0.05 wt.% nanocomposites concentration. Based on better results, ENC nanofluid at salinity concentrations of 10 SW and 2 SW ENC were selected for the EOR of carbonate rocks under reservoir conditions. The contact angles of ENC nanocomposites at the salinities of 2 SW and 10 SW were 49 and 43.4°, respectively. Zeta potential values were -44.39 and -46.58 for 2 SW and 10 SW ENC nanofluids, which is evidence of the high stability of ENC nanocomposites. The imbibition results at 70 °C and 2000 psi with 0.05 wt.% ENC at 10 SW and 2 SW led to incremental oil recoveries of 64.13% and 60.12%, respectively, compared to NC, which was 46.16%.

Experimental analyzing the effect of n-heptane concentration and angular frequency on the viscoelastic behavior of crude oil containing asphaltene.

Asphaltene often produces problems in upstream and downstream sections of crude oil transportation and processing equipment. These issues are directly related to the asphaltene precipitation in transportation pipelines, separation columns, heat exchangers, and storage tanks. This research investigates the impact of angular frequency and n-heptane concentration on asphaltene precipitation and rheological behavior of two oil samples from the Mansouri oil field in Iran, i.e., 23 and 71. The viscosity tests revealed that these oil samples and their mixtures with n-heptane exhibit Newtonian behavior. Moreover, increasing the n-heptane concentration increases the asphaltene precipitation and dramatically decreases crude oil viscosity. The frequency tests revealed that the presence of n-heptane has an unfavorable effect on crude oil's viscoelastic behavior. Therefore, it is necessary to find the optimum range of angular frequency and n-heptane concentration to minimize the asphaltene content of crude oil and provide them with appropriate viscoelastic behavior. Increasing the angular frequency continuously increases all oil samples' loss modulus and strengthens their liquid-like manner. The experimental results confirmed that the angular frequency higher than 33.6 rad/s and 75% volume concentration of n-heptane is the best condition for the oil sample of 23. On the other hand, the angular frequency higher than 23.4 rad/s and 75% volume concentration of n-heptane is the best condition for the oil sample of 71. In these conditions, the oil samples of 23 and 71 not only have appropriate viscoelastic behavior, but they also experience 97.2% and 96.3% reductions in their viscosity, respectively.

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids.

Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs.

In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reservoir models with a classification accuracy higher than 79%. In addition, the statistical analysis approves that the optimized regressors accurately perform the reservoir characterization with mean relative errors of lower than 4.5%. The minimized manual interference reduces human bias, and the models have significant noise tolerance for practical applications.

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies.

Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector regressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R2) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained.

Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model.

Synthesizing micro-/nano-sized pharmaceutical compounds with an appropriate size distribution is a method often followed to enhance drug delivery and reduce side effects. Supercritical CO2 (carbon dioxide) is a well-known solvent utilized in the pharmaceutical synthesis process. Reliable knowledge of a drug's solubility in supercritical CO2 is necessary for feasible study, modeling, design, optimization, and control of such a process. Therefore, the current study constructs a stacked/ensemble model by combining three up-to-date machine learning tools (i.e., extra tree, gradient boosting, and random forest) to predict the solubility of twelve anticancer drugs in supercritical CO2. An experimental databank comprising 311 phase equilibrium samples was gathered from the literature and applied to design the proposed stacked model. This model estimates the solubility of anticancer drugs in supercritical CO2 as a function of solute and solvent properties and operating conditions. Several statistical indices, including average absolute relative deviation (AARD = 8.62%), mean absolute error (MAE = 2.86 × 10-6), relative absolute error (RAE = 2.42%), mean squared error (MSE = 1.26 × 10-10), and regression coefficient (R2 = 0.99809) were used to validate the performance of the constructed model. The statistical, sensitivity, and trend analyses confirmed that the suggested stacked model demonstrates excellent performance for correlating and predicting the solubility of anticancer drugs in supercritical CO2.