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Accurate prediction of biochar yield from biomass pyrolysis is essential for optimizing production in sustainable agriculture, yet remains technically challenging due to multiple interacting factors. This study developed a predictive framework using a curated dataset of 211 samples, each including 14 normalized input features (chemical, physical, operational) and one output variable (biochar yield, wt%). Machine learning modeling utilized Gradient Boosted Decision Trees (GBDT), with hyperparameters exhaustively tuned via Gaussian Processes Optimization (GPO), Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), and Batch Bayesian Optimization (BBO). Models were evaluated on a train-test split (90% training, 10% testing) and the best performance was achieved by the GBDT–BPI model: total R² = 0.982, mean squared error (MSE) = 1.65, average absolute relative error percentage (AARE%) = 1.35; on the test set, R² = 0.693, MSE = 15.2, AARE% = 9.54. Comparative analysis showed GBDT–BPI outperformed GBDT–GPO (total R² = 0.978; MSE = 2.01; AARE% = 1.72), GBDT–ES (total R² = 0.976; MSE =  2.13; AARE% = 3.81), and GBDT–BBO (total R² = 0.980; MSE = 1.81; AARE% = 2.58). Sensitivity study presented reside duration, temperature, and fixed carbon as the top parameters of yield. Time efficiency was comparable for all optimizers, with BBO taking the longest (313 s/500 iterations). Diagnostic leverage analysis demonstrated high data quality, with less than 1% flagged as influential outliers. This integrated approach delivered high-accuracy, interpretable prediction, and revealed critical parameters for process optimization in biomass pyrolysis workflows.

Efficient extraction of subsurface resources relies heavily on the performance of drilling fluids, which necessitates constant innovation in their formulation. This study introduces a novel Polylactic Acid/Henna composite for significant enhancement of drilling fluid properties. The composites was characterized via Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and Fourier-Transform Infrared Spectroscopy (FTIR), confirming the uniform Henna particles dispersion in PLA matrix. The composite was added to water-based drilling fluids at concentrations of 0.5 wt%, 1 wt%, 2 wt%, 4 wt%, and 10 wt%, followed by rigorous evaluation of rheological and filtration performance. Experimental results demonstrated that fluids containing 2 wt% PLA/Henna composite exhibited the best performance, with a 32% increase in yield point and a 21% improvement in plastic viscosity compared to the base fluid. Furthermore, filtration volume decreased by 42%, while spurt loss was reduced by 35% due to improved filter cake formation. These quantitative improvements optimize fluid efficiency and minimize permeability, enhancing the ability to control fluid loss under simulated drilling conditions. Such enhancements promote better wellbore stability and operational reliability.

Polyethylene glycol (PEG), a synthetic polymer made up of repeating ethylene oxide units, is widely recognized for its broad utility and adaptable properties. Precise estimation of CO 2 solubility in PEG plays a vital role in enhancing processes such as supercritical fluid extraction, carbon capture, and polymer modification, where CO 2 serves as a solvent or transport medium. This study focuses on building advanced predictive models using machine-learning approaches, such as random forest (RF), decision tree (DT), adaptive boosting (AdaBoost), k-nearest neighbors (KNN), and ensemble learning (EL) to forecast CO 2 solubility in PEG across a wide range of conditions. The data utilized for model development is sourced from previously published literature, and an outlier detection method is applied beforehand to identify any suspicious data points. Additionally, sensitivity analysis is performed to evaluate the relative influence of each input parameter on the output variable. The results proved that DT model is the most performance method for estimating CO 2 solubility in PEG since it showed largest R-squared (i.e., 0.801 and 0.991 for test and train, respectively) and lowest error metrics (MSE: 0.0009 and AARE%: 22.58 for test datapoints). In addition, it was found that pressure and PEG molar mass directly affects the solubility in contrast to the temperature variable which has an inverse relationship. The developed DT model can be regarded accurate and robust user-friendly tool for estimating CO 2 solubility in PEG without needing experimental workflows which are known to be time-consuming, expensive and tedious.

Mud loss during drilling operations poses a significant problem in the oil and gas industry due to its contributions to increased costs and operational risks. This study aims to develop a reliable predictive model for mud loss volume using machine learning techniques to improve drilling efficiency and reduce non-productive time. The dataset consists of 949 field records from Middle Eastern drilling sites, incorporating variables such as borehole diameter, drilling fluid viscosity, mud weight, solid content, and pressure differential. Initial data analysis included statistical evaluation, outlier detection using leverage diagnostics, and data normalization to ensure validity and consistency. A Gradient Boosting Machine (GBM) served as the core predictor, with its hyperparameters fine-tuned using four optimization strategies: Evolution Strategies (ES), Batch Bayesian Optimization (BBO), Bayesian Probability Improvement (BBI), and Gaussian Process Optimization (GPO). Model performance was evaluated using k-fold cross-validation, with metrics including R², mean squared error and average absolute relative error percentage. Results demonstrated that the GBM-BPI achieved the strongest test performance (R² = 0.926, MSE = 1208.77, AARE% = 26.73), outperforming other approaches in accuracy and stability. Feature importance assessed through SHAP analysis revealed that hole size, formation type, and pressure differential were the most influential variables, while solid content had minimal effect.

Coke strength after reaction (CSR) is a critical parameter in metallurgical applications, and its accurate prediction is essential for optimizing coal blends and coking processes. This research develops a data-driven method to model CSR by eight input variables, including moisture content, volatile matter, ash percentage, sulfur content, maximum fluidity, plastic layer thickness, mean maximum reflectance (MMR), and the basicity index. A dataset comprising 630 coal samples with diverse properties was analyzed using advanced techniques such as Pearson correlation analysis, the Monte Carlo outlier detection technique in data integrity assessment, and machine-learning models with five-fold cross-validation. Multiple algorithms were implemented, including random forests, decision trees, adaptive boosting, convolutional neural networks, support vector regression, multilayer perceptron-artificial neural networks, and an ensemble learning approach, with hyperparameter optimization and evaluation metrics like mean squared error, R2, and mean and average absolute relative error. The random forest model decisively outperformed all other contenders, demonstrating its superior predictive power through consistently high R2 values and minimal error rates. Furthermore, Shapley additive explanations analysis revealed the influence of each input variable, with volatile matter having a predominantly negative effect on CSR, while features like MMR and moisture showed positive correlations. This systematic methodology underscores the importance of robust data assessment and machine-learning models in enhancing predictive accuracy for CSR.

Precisely forecasting coke reactivity index (CRI) plays a critical role in the metallurgical industry, as it enables optimization of coke quality, leading to cost-effective production and efficient resource utilization. In this research, several machine learning predictive models based on extra trees, decision tree, support vector machine, random forest, multilayer perceptron artificial neural network, K-nearest neighbors, convolutional neural network, ensemble learning, and adaptive boosting using a dataset gathered from a coke plant are developed to predict CRI. To minimize overfitting in each algorithm, K-fold cross-validation methodology is employed during the training phase. The efficacy of each algorithm is visually represented through graphical methods and quantitatively evaluated using performance metrics. The findings indicate that maximum fluidity and mean maximum reflectance (MMR) exhibit a direct correlation with CRI while being indirectly relevant to moisture content, ash content, sulfur content, basicity index, plastic layer thickness, and MMR. Among the various predictive models evaluated, the random forest model emerged as the most accurate tool, according to the performance metrics of R-squared, mean square error, and average absolute relative error (%), with numerical values of 0.958, 3.718, and 2.545%, respectively, for the total datapoints. The developed tool can be easily used to accurately estimate CRI without needing experimental or field data reliably.

Mitigating formation damage due to fines migration is crucial for maintaining reservoir productivity in enhanced oil recovery (EOR) processes. This research introduces a novel composite, Titanium dioxide nanoparticles coated with Saponin and Zirconium (TiO 2 @Saponin/Zr(IV)), synthesized via a sol–gel method, to address this challenge, particularly in low salinity water injection scenarios. Characterization through FT-IR confirmed successful functionalization, indicated by the Zr–O band at 480 cm −1 and saponin bands around 1030–1085 cm −1 and 2919–2850 cm −1 . Zeta potential measurements showed that in low salinity brine, quartz and kaolinite exhibited highly negative potentials of − 32 mV and − 45 mV, respectively, while TiO 2 @Saponin/Zr(IV) displayed a positive potential of + 19 mV. Importantly, mixtures of quartz and kaolinite with TiO 2 @Saponin/Zr(IV) in low salinity conditions resulted in moderated zeta potentials of + 3 mV and − 2 mV, indicating surface charge modulation. Core flooding experiments further validated the composite’s effectiveness. Injecting high salinity water resulted in a minor permeability reduction from 90 to 78 mD, while low salinity water injection caused a drastic drop from 90 to 8 mD. However, with the introduction of 0.5 wt% TiO 2 @Saponin/Zr(IV) in low salinity water, the permeability reduction was significantly controlled, decreasing from 90 to 85 mD. These quantitative results demonstrate that TiO 2 @Saponin/Zr(IV) effectively mitigates fines migration by modifying surface charge and preserving permeability, offering a promising solution for formation damage control and enhanced oil recovery.

The accurate estimation of caloric density in food products is a critical component of nutritional science and dietary management, yet experimental determination remains resource intensive. The research develops a robust computational framework for predicting caloric energy based on standard nutritional composition variables using advanced machine learning techniques. To achieve this, a dataset comprising 410 food items with seven predictors, including protein, fat, carbohydrates, sugar, dietary fiber, sodium, and potassium, was utilized to train a Gradient Boosting Decision Trees (GBDT) model. The study evaluated the efficacy of four exceptional hyperparameter optimization algorithms: BBO, BPI, GPO, and evolutionary strategies (ES). Performance was rigorously assessed using 5‐fold cross‐validation and statistical metrics including R2, MSE, and AARE%. The results demonstrated that the GBDT‐ES configuration achieved the best performance with a test R2 of 0.982950 and an AARE% of 3.661596%, whereas GBDT‐GPO offered a competitive balance of accuracy and computational efficiency with the lowest runtime. Furthermore, SHAP analysis revealed that carbohydrates and fats were the primary drivers of caloric estimation, ensuring the model aligned with biological energy densities. In conclusion, the integration of evolutionary optimization with gradient boosting provides a highly precise and scientifically interpretable tool for nutritional analysis, offering a viable alternative to traditional laboratory calorimetry. The research develops a robust computational framework for predicting caloric energy based on standard nutritional composition variables using advanced machine learning techniques

Efficient and sustainable exploitation of geothermal energy depends critically on accurate characterization of reservoir permeability, which governs subsurface fluid flow and thermal performance. While well testing and core analysis remain essential for establishing ground-truth permeability, these methods can be costly and limited in spatial resolution, making it challenging to fully capture the fine-scale heterogeneity and fracture complexity characteristic of geothermal formations. Moreover, standard Nuclear Magnetic Resonance (NMR)-based permeability models, while widely used in hydrocarbon reservoirs, tend to underperform under geothermal conditions due to elevated temperatures and high fluid salinity. To address these challenges, this study proposes a novel data-driven framework for predicting absolute permeability in geothermal rocks using NMR laboratory measurements and advanced machine learning algorithms. A curated dataset of 72 core samples from the GBD4 geothermal well (Catinat M et al. in Geothermics 111:102707, 2023) was used, incorporating porosity, lithology, the logarithmic mean relaxation time (T2lm), and the mode of the relaxation time distribution (T2mode) as input features. Eight models were developed: Decision Trees, AdaBoost, K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Ensemble Learning, Convolutional Neural Network (CNN), Support Vector Regression (SVR), and Random Forest. Outlier detection was performed using the Leverage method, and model robustness was validated via K-fold cross-validation. Among all models, MLP-ANN achieved the highest predictive accuracy with a test R 2 of 0.943 and a test RMSE of 68.52. Importantly, this study differs from prior NMR–ML permeability models by explicitly validating performance under geothermal temperature–salinity conditions. The results demonstrate that porosity is the most influential predictor of permeability, as confirmed by both Pearson correlation and SHAP analysis. This study integrates empirical core analysis with computational modeling, delivering a scalable and economical substitute for conventional laboratory techniques while propelling advancements in intelligent petrophysical characterization.

Mitigating formation damage due to fines migration is crucial for maintaining reservoir productivity in enhanced oil recovery (EOR) processes. This research introduces a novel composite, Titanium dioxide nanoparticles coated with Saponin and Zirconium (TiO @Saponin/Zr(IV)), synthesized via a sol-gel method, to address this challenge, particularly in low salinity water injection scenarios. Characterization through FT-IR confirmed successful functionalization, indicated by the Zr-O band at 480 cm and saponin bands around 1030-1085 cm and 2919-2850 cm . Zeta potential measurements showed that in low salinity brine, quartz and kaolinite exhibited highly negative potentials of - 32 mV and - 45 mV, respectively, while TiO @Saponin/Zr(IV) displayed a positive potential of + 19 mV. Importantly, mixtures of quartz and kaolinite with TiO @Saponin/Zr(IV) in low salinity conditions resulted in moderated zeta potentials of + 3 mV and - 2 mV, indicating surface charge modulation. Core flooding experiments further validated the composite's effectiveness. Injecting high salinity water resulted in a minor permeability reduction from 90 to 78 mD, while low salinity water injection caused a drastic drop from 90 to 8 mD. However, with the introduction of 0.5 wt% TiO @Saponin/Zr(IV) in low salinity water, the permeability reduction was significantly controlled, decreasing from 90 to 85 mD. These quantitative results demonstrate that TiO @Saponin/Zr(IV) effectively mitigates fines migration by modifying surface charge and preserving permeability, offering a promising solution for formation damage control and enhanced oil recovery.