Modeling interfacial tension of methane-brine systems at high pressure and high salinity conditions

The precipitation and deposition of asphaltene on solid surfaces present a significant challenge throughout all stages of petroleum recovery, from hydrocarbon reservoirs in porous media to wellbore and transfer pipelines. A comprehensive understanding of asphaltene aggregation phenomena is crucial for controlling deposition issues. In addition to experimental studies, accurate prediction of asphaltene aggregation kinetics, which has received less attention in previous research, is essential. This study proposes an artificial intelligence-based framework for precisely predicting asphaltene particle aggregation kinetics. Different techniques were utilized to predict the asphaltene aggregate diameter as a function of pressure, temperature, oil specific gravity, and oil asphaltene content. These methods included the adaptive neuro-fuzzy interference system (ANFIS), radial basis function (RBF) neural network optimized with the Grey Wolf Optimizer (GWO) algorithm, extreme learning machine (ELM), and multi-layer perceptron (MLP) coupled with Bayesian Regularization (BR), Levenberg–Marquardt (LM), and Scaled Conjugate Gradient (SCG) algorithms. The models were constructed using a series of published data. The results indicate the excellent correlation between predicted and experimental values using various models. However, the GWO-RBF modeling strategy demonstrated the highest accuracy among the developed models, with a determination coefficient, average absolute relative deviation percent, and root mean square error (RMSE) of 0.9993, 1.1326%, and 0.0537, respectively, for the total data.

“…Specifically, if a step is successful, is decreased, whereas if a step is unsuccessful, is increased. Typically, the cost function decreases with each step 57 , 63 .…”

Section: Model Developmentmentioning

confidence: 99%

“…The BR algorithm endeavors to build a suitable network by minimizing the sum of weights and squared errors, as indicated in equation 65 . Once the optimal values for and have been determined, the algorithm employs algebraic manipulation to utilize the LM algorithm to minimize the objective function 57 , 63 .…”

Section: Model Developmentmentioning

confidence: 99%

Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery

Sharifzadegan,

Behnamnia,

Dehghan Monfared

2023

“…Algorithmic modeling culture is now widely used in statistics and data analysis 29 – 32 . Unlike the traditional approach that uses a stochastical model, the algorithmic culture uses a complex algorithm 33 , 34 .…”

Section: Introductionmentioning

confidence: 99%

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

Maghsoudy,

Zakerabbasi,

Baghban

et al. 2024

The AB2 metal hydrides are one of the preferred choices for hydrogen storage. Meanwhile, the estimation of hydrogen storage capacity will accelerate their development procedure. Machine learning algorithms can predict the correlation between the metal hydride chemical composition and its hydrogen storage capacity. With this purpose, a total number of 244 pairs of AB2 alloys including the elements and their respective hydrogen storage capacity were collected from the literature. In the present study, three machine learning algorithms including GA-LSSVM, PSO-LSSVM, and HGAPSO-LSSVM were employed. These models were able to appropriately predict the hydrogen storage capacity in the AB2 metal hydrides. So the HGAPSO-LSSVM model had the highest accuracy. In this model, the statistical factors of R2, STD, MSE, RMSE, and MRE were 0.980, 0.043, 0.0020, 0.045, and 0.972%, respectively. The sensitivity analysis of the input variables also illustrated that the Sn, Co, and Ni elements had the highest effect on the amount of hydrogen storage capacity in AB2 metal hydrides.

“…These methods are usually restricted to a given system and composition, as well as a defined temperature and pressure range. Systems based on Artificial Intelligence (Al) and Machine Learning (ML) approaches can be appropriate and vigorous in calculating the H 2 S solubility in ILs since they can be constructed on data from an extensive range of materials under varied thermodynamic circumstances, particularly if a thorough database is utilized for model building 36 – 38 . Furthermore, AI and ML approaches have improved precision and generality when modeling a variety of phenomena in science and engineering domains such as environmental engineering 39 , petroleum engineering 40 – 45 , physical chemistry 46 – 48 , civil engineering 49 , earth science 49 , and chemical engineering 46 , 49 – 56 .…”

Section: Introductionmentioning

confidence: 99%

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches

Mousavi

Nakhaei-Kohani

Atashrouz

et al. 2023

Self Cite

In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H2S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H2S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H2S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (Tc) and pressure (Pc), acentric factor (ω), boiling temperature (Tb), and molecular weight (Mw), were used in these models; the output was the solubility of H2S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R2) of 0.99, provides more precise calculations for H2S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H2S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H2S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H2S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H2S.