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Abstract The urgency to mitigate carbon dioxide (CO2) emissions and combat climate change has spurred the development of effective CO2 capture technologies. One of the industry’s most well-known CO2 collection processes is CO2 absorption utilizing amine solvents. However, designing a successful amine scrubbing system in power plants requires precise prediction of CO2 absorption in aqueous amine solutions under various operating circumstances. Using aqueous piperazine (PZ) solutions for chemical absorption is promising due to the favorable reactivity of PZ with CO2. In this study, a comprehensive evaluation of PZ solution performance in CO2 capturing, employing the white-box algorithms, namely, Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH), was performed. Through extensive experimentation and data analysis, several correlations were developed with high and acceptable R2 values, such as 0.933 for GP, 0.949 for GEP, and 0.889 for GMDH, which shows high accuracy and reliability in predicting the CO2 capture efficiency of PZ solutions under varying operating conditions. The results of sensitivity analysis revealed that CO2 partial pressure increased CO2 absorption, while PZ concentration and temperature had negative and decreasing effects. These insights provide essential guidance for optimizing process conditions to enhance the CO2 capture efficiency. Finally, the leverage method was used to assess the reliability of both experimental and predicted data from white-box algorithms. This analysis identified potential outliers and validated the accuracy of the model’s predictions, enhancing the credibility of developed correlations and demonstrating the robustness of the approach.

The urgency to mitigate carbon dioxide (CO2) emissions and combat climate change has spurred the development of effective CO2 capture technologies. One of the industry’s most well-known CO2 collection processes is CO2 absorption utilizing amine solvents. However, designing a successful amine scrubbing system in power plants requires precise prediction of CO2 absorption in aqueous amine solutions under various operating circumstances. Using aqueous piperazine (PZ) solutions for chemical absorption is promising due to the favorable reactivity of PZ with CO2. In this study, a comprehensive evaluation of PZ solution performance in CO2 capturing, employing the white-box algorithms, namely, Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH), was performed. Through extensive experimentation and data analysis, several correlations were developed with high and acceptable R2 values, such as 0.933 for GP, 0.949 for GEP, and 0.889 for GMDH, which shows high accuracy and reliability in predicting the CO2 capture efficiency of PZ solutions under varying operating conditions. The results of sensitivity analysis revealed that CO2 partial pressure increased CO2 absorption, while PZ concentration and temperature had negative and decreasing effects. These insights provide essential guidance for optimizing process conditions to enhance the CO2 capture efficiency. Finally, the leverage method was used to assess the reliability of both experimental and predicted data from white-box algorithms. This analysis identified potential outliers and validated the accuracy of the model’s predictions, enhancing the credibility of developed correlations and demonstrating the robustness of the approach.Article Highlights: Aqueous piperazine solutions show promise, with white-box algorithms predicting high efficiency (R2 values: GP 0.933, GEP 0.949, GMDH 0.889). Insights from sensitivity analysis indicate that increasing CO2 partial pressure enhances absorption, while higher PZ concentration and temperature have decreasing effects. The study's use of white-box algorithms, including Genetic Programming and Group Method of Data Handling, demonstrates a reliable approach for predicting and optimizing CO2 capture in power plants. [ABSTRACT FROM AUTHOR]

Abstract Carbon dioxide (CO2) is the main greenhouse gas that drives global warming, climate change, and other environmental issues. CO2 absorption using amine solvents stands out as one of the most well-known industrial technologies of CO2 capture. However, accurate prediction of CO2 absorption in aqueous amine solutions under different operating conditions is crucial for designing an efficient amine scrubbing system in power plants. In this work, CO2 solubility in aqueous piperazine (PZ) solutions was modeled using 517 experimental data points covering a temperature range of 298 to 373 K, PZ concentration of 0.1 to 6.2 mol/L (M), and CO2 partial pressure of 0.03 to 7399 kPa. To this end, four robust machine learning algorithms, including gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and adaptive boosting decision trees (AdaBoost-DT) were utilized. Among the developed models, the CatBoost model presented the highest accuracy with an overall determination coefficient (R2) of 0.9953 and an average absolute relative error of 2.36%. Sensitivity analysis revealed that CO2 partial pressure had the greatest influence on CO2 absorption in aqueous PZ solutions, followed by PZ concentration and temperature. Moreover, CO2 partial pressure positively influenced CO2 absorption in aqueous PZ solutions, while PZ concentration and temperature exhibited negative effects. Finally, the leverage technique indicated that both the experimental data bank used for modeling and the model’s estimates were statistically acceptable and valid showing only 8 points (∼1.5% of total data) as possible suspected data.

Objective Thoracolumbar compression fractures resulting from high‐energy injuries are a common type of spinal fracture. Satisfying reduction of compressive vertebra body is essential for the clinical outcome. However, traditional distraction technique may lead to complications including pedicle screw loosening, pedicle screw breakage, and postoperative back pain because of excessive distraction. In this study, we reported a novel technique for reduction. Additionally, the effect and postoperative radiological parameters of this technique were compared with those of traditional distraction technique. Methods The clinical data of 80 patients who had been treated with posterior pedicle screw fixation from January 2019 to December 2020 was retrospectively analyzed. Thirty‐six patients were performed with the leverage technique, while 22 patients were treated with the traditional distraction technique. When pedicle screw fixation was performed with either the leverage technique or the traditional distraction technique, fracture reduction was completed with monoaxial pedicle screws using either the leverage maneuver or distraction of adjacent vertebrae. Clinical evaluation, including operation time, hospital stay, blood loss volume, and postoperative complications were collected. The American Spinal Injury Association (ASIA) score for neurological condition and the visual analog scale (VAS) score for pain were used to evaluate the patients' functional outcome. The radiographic analysis included local kyphotic angle (LKA), regional kyphotic angle (RKA), anterior vertebral height (AVH), posterior vertebral height (PVH), and sagittal compression (SC). The student t‐test and the χ2‐test (or the Fisher exact test) were used to compare the outcome measures between the two groups. Results The leverage group comprised 36 patients, while 44 patients were included in the distraction group. No statistically significant differences were found in the demographic data. The VAS score in the leverage group (3.0 ± 0.8) was significantly lower than that in the distraction group (4.2 ± 0.6) on postoperative day 1. Total correction of the LKA in the leverage group (11.5 ± 2.5°) was significantly higher than that in the distraction group (7.1 ± 1.3°) (p = 0.0004). Total correction of the RKA was higher in the leverage group (12.1 ± 4.3°) than in the distraction group (6.1 ± 0.9°) (p = 0.005). The ratio of rear pillar /front pillar correction was 0.35 ± 0.13 and 0.89 ± 0.18 in the leverage and distraction groups, respectively (p = 0.014). Total correction of the upper and lower foraminal height in the leverage group was significantly less than that in the distraction group. The leverage group had significantly higher correction of the upper and lower intervertebral space height than the distraction group. Conclusions Our novel leverage technique provided better kyphotic reduction and restoration than compared to conventional distraction technique in the surgical treatment of thoracolumbar compression fractures.

Objective: Thoracolumbar compression fractures resulting from high‐energy injuries are a common type of spinal fracture. Satisfying reduction of compressive vertebra body is essential for the clinical outcome. However, traditional distraction technique may lead to complications including pedicle screw loosening, pedicle screw breakage, and postoperative back pain because of excessive distraction. In this study, we reported a novel technique for reduction. Additionally, the effect and postoperative radiological parameters of this technique were compared with those of traditional distraction technique. Methods: The clinical data of 80 patients who had been treated with posterior pedicle screw fixation from January 2019 to December 2020 was retrospectively analyzed. Thirty‐six patients were performed with the leverage technique, while 22 patients were treated with the traditional distraction technique. When pedicle screw fixation was performed with either the leverage technique or the traditional distraction technique, fracture reduction was completed with monoaxial pedicle screws using either the leverage maneuver or distraction of adjacent vertebrae. Clinical evaluation, including operation time, hospital stay, blood loss volume, and postoperative complications were collected. The American Spinal Injury Association (ASIA) score for neurological condition and the visual analog scale (VAS) score for pain were used to evaluate the patients' functional outcome. The radiographic analysis included local kyphotic angle (LKA), regional kyphotic angle (RKA), anterior vertebral height (AVH), posterior vertebral height (PVH), and sagittal compression (SC). The student t‐test and the χ2‐test (or the Fisher exact test) were used to compare the outcome measures between the two groups. Results: The leverage group comprised 36 patients, while 44 patients were included in the distraction group. No statistically significant differences were found in the demographic data. The VAS score in the leverage group (3.0 ± 0.8) was significantly lower than that in the distraction group (4.2 ± 0.6) on postoperative day 1. Total correction of the LKA in the leverage group (11.5 ± 2.5°) was significantly higher than that in the distraction group (7.1 ± 1.3°) (p = 0.0004). Total correction of the RKA was higher in the leverage group (12.1 ± 4.3°) than in the distraction group (6.1 ± 0.9°) (p = 0.005). The ratio of rear pillar /front pillar correction was 0.35 ± 0.13 and 0.89 ± 0.18 in the leverage and distraction groups, respectively (p = 0.014). Total correction of the upper and lower foraminal height in the leverage group was significantly less than that in the distraction group. The leverage group had significantly higher correction of the upper and lower intervertebral space height than the distraction group. Conclusions: Our novel leverage technique provided better kyphotic reduction and restoration than compared to conventional distraction technique in the surgical treatment of thoracolumbar compression fractures. [ABSTRACT FROM AUTHOR]

Introduction: Anterior shoulder dislocations are commonly seen in the emergency department for which several closed reduction techniques exist. The aim of this systematic review is to identify the most successful principle of closed reduction techniques for an acute anterior shoulder dislocation in the emergency department without the use of sedation or intra-articular lidocaine injection. Methods: A literature search was conducted up to 15-08-2022 in the electronic databases of PubMed, Embase and CENTRAL for randomized and observational studies comparing two or more closed reduction techniques for anterior shoulder dislocations. Included techniques were grouped based on their main operating mechanism resulting in a traction–countertraction (TCT), leverage and biomechanical reduction technique (BRT) group. The primary outcome was success rate and secondary outcomes were reduction time and endured pain scores. Meta-analyses were conducted between reduction groups and for the primary outcome a network meta-analysis was performed. Results: A total of 3118 articles were screened on title and abstract, of which 9 were included, with a total of 987 patients. Success rates were 0.80 (95% CI 0.74; 0.85), 0.81 (95% CI 0.63; 0.92) and 0.80 (95% CI 0.56; 0.93) for BRT, leverage and TCT, respectively. No differences in success rates were observed between the three separate reduction groups. In the network meta-analysis, similar yet more precise effect estimates were found. However, in a post hoc analysis the BRT group was more successful than the combined leverage and TCT group with a relative risk of 1.33 (95% CI 1.19, 1.48). Conclusion: All included techniques showed good results with regard to success of reduction. The BRT might be the preferred technique for the reduction of an anterior shoulder dislocation, as patients experience the least pain and it results in the fastest reduction. [ABSTRACT FROM AUTHOR]

Having accurate information about the solubility of hydrogen (H 2) in alcoholic solvents is crucial for the design and implementation of numerous chemical processes. In this communication, two robust correlative techniques, Genetic programming (GP) and Group method of data handling (GMDH) were used to estimate H 2 solubility in alcohols. For the mentioned purpose, 673 laboratory data of H 2 solubility for 26 distinct alcoholic solvents were collected over a broad interval of operating pressure from 0.101 MPa to 110.3 MPa and temperature from 213.15 K to 524.9 K. These solvents include fatty alcohols, aliphatic alcohols, diols, glycols, and hydroxypolyether with molecular weights ranging from 32.042 to 242.446 g/mol. The algorithms' input parameters were selected to be molecular weight of alcohol, the temperature and pressure of the solubility system, critical temperature and pressure of alcohols. According to the graphical and statistical assessments, the GMDH model was shown to be the best choice for estimating H 2 solubility in alcoholic solvents, with a root mean square error of 0.00482 and a coefficient of determination of 0.9841. Furthermore, according to sensitivity analysis, the greatest influence on H 2 solubility in alcoholic solvents is dedicated to pressure, temperature, and molecular weight of alcohols. Furthermore, the Leverage technique was used to identify the application domain of the GMDH model and outlier data, with the findings indicating that GMDH has a high credit for estimating H 2 dissolution in alcoholic media. • H 2 solubility in 26 distinct alcohols are predicted using White-box approaches. • GP and GMDH methods are developed using 673 experimental datapoints. • The GMDH model exhibits higher accuracy (RMSE = 0.00482 and R2 = 0.98). • Pressure has the dominant effect on H 2 solubility (relative importance = 53%). [ABSTRACT FROM AUTHOR]

As a result of technological advancements, reliable calculation of hydrogen (H 2) solubility in diverse hydrocarbons is now required for the design and efficient operation of processes in chemical and petroleum processing facilities. The accuracy of equations of state (EOSs) in estimating H 2 solubility is restricted, particularly in high-pressure or/and high-temperature conditions, which could result in energy loss and/or potential safety and environmental problem. Two strong machine learning techniques for building advanced correlation were used to evaluate H 2 solubility in hydrocarbons in this study which were Group method of data handling (GMDH) and genetic programming (GP). For that purpose, 1332 datasets from experimental results of H 2 solubility in 32 distinct hydrocarbons were collected from 68 various systems throughout a wide range of operating temperatures from 98 K to 701 K and pressures from 0.101325 MPa to 78.45 MPa. Hydrocarbons from two distinct classes include alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. Hydrocarbons have a molecular mass range of 28.054–647.2 g/mol, which corresponds to a carbon number of 2–46. Solvent molecular weight, critical pressure, and critical temperature, as well as pressure and temperature operational parameters, were used to create the features. With a regression coefficient (R2) which was equal to 0.986 and root mean square error (RMSE) which was 0.0132, the GP modeling approach estimated experimental solubility data more accurately than the GMDH approach. Operating pressure, followed by molecular weight of hydrocarbon solvents and temperature, had the greatest influence on estimation H 2 solubility, according to sensitivity analysis. The GP model shown in this paper is a reliable development that may be used in the chemical and petroleum sectors as a reliable and effective estimator for H 2 solubility in diverse hydrocarbons. • Solubility of hydrogen in various hydrocarbons is predicted. • White-box machine learning approaches comprising GMDH and GP are implemented. • GP estimated experimental solubility data more accurately than the GMDH. • Sensitivity analysis highlighted the effect of operating pressure on solubility. [ABSTRACT FROM AUTHOR]

In this work, three optimized intelligent models on the basis of multilayer perceptron (MLP), two radial basis function (RBF) models (including RBF- particle swarm optimization (PSO) and RBF- farmland fertility algorithm (FFA)), and a committee machine intelligent system (CMIS) were used to estimate Thermal Conductivity (TC) of hybrid nanofluids. Base fluid TC, TC of first and second nanoparticles, size of first and second nanoparticles, volume fractions of first and second nanoparticles, and temperature were considered as inputs to model TC of hybrid nanofluids. Based on the results, the CMIS model emerged as the most accurate model for predicting TC coefficient of hybrid nanofluids, boasting an average absolute percent relative error (AAPRE) of just 1.064%. Comparison with various empirical and theoretical models further highlighted the superior predictive performance of the CMIS model in assessing the TC of hybrid nanofluids. Ultimately, the leverage technique underscored the reliability of the CMIS model. [Display omitted] • Thermal conductivity of hybrid nanofluids is modeled using an extensive data bank. • MLP and RBF are used for modeling and then they are integrated inro a CMIS model. • MLP, SCG, and BR are used for MLP training and PSO and FFA are applied for RBF optimization. • The CMIS predicts all the data with average absolute percent relative error of 1%. • The leverage approach confirms the reliability of the developed CMIS model. [ABSTRACT FROM AUTHOR]

Asphaltene precipitation in oil operations contributes to serious technical and non-technical issues, which is affected by reservoir conditions such as pressure, temperature, dilution ratio, and type of diluent. In this work, a mathematical correlation is introduced to forecast the weight percent (wt%, g/g) of precipitated asphaltene in light oils. Experimental data are obtained based on high-resolution images taken from high-pressure cell processed by the image analysis method. Employing experimental data, a simple and precise correlation on the basis of the scaling/fractal theory is developed. In this study, the amount of asphaltene precipitation is considered in terms of pressure and dilution ratio in which the coefficients are simply calculated through the Vandermone matrix. Comparison between the model results and real data reveals a good match where the average coefficient of correlation (R2) is above 0.95. Additionally, two previous predictive methods, namely the Bayesian belief network and scaling models, are utilized and the outputs are compared (e.g. the magnitudes of mean squared error (MSE) are 0.0065 and 0.038 for C6 and C7, respectively), implying the effectiveness of the new deterministic tool in terms of accuracy and reliability such that the average MSE is 0.006 for both C6 and C7. The trustworthiness of the real asphaltene precipitation data is also assessed by the statistical leverage approach based on the hat matrix, standardized residuals, and William plot to specify the applicability domain (AD) of the predictive models. It was found that all deterministic techniques presented in this study result in satisfactory accuracy, since the entire collected experimental data are reliable within AD. [ABSTRACT FROM AUTHOR]