Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 327, P. 119544 - 119544

Published: Jan. 24, 2025

Language: Английский

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Harnessing Machine Learning for Enhanced Thermal Insulation and Energy Efficiency in Buildings Worldwide

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104086 - 104086

Published: Jan. 1, 2025

Language: Английский

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Ecofriendly dyeing of silk fabric with yellow natural and synthetic dye

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104192 - 104192

Published: Jan. 1, 2025

Language: Английский

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Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 18, 2025

The contamination of water and soils with heavy metals poses a significant environmental threat, making the development effective removal strategies global priority. Hence, determination can play an essential role in monitoring assessment. In current research, ensemble machine learning (ML) models (i.e., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient (GB), HistGradientBoosting, Extreme (XGBoost), Light Gradient-Boosting Machine (LightGBM)) were applied attempt to predict adsorption efficiency several Pb, Cd, Ni, Cu, Zn) according different factors including temperature, pH, biochar characteristics. Data collected from open-source literature review 353 samples. At first stage, data processing was performed outliers' scaling for better modeling applicability; whereas, second stage predictive conducted. results showed that XGBoost model attained superior accuracy comparison other by achieving highest coefficient (R2 = 0.92). research extended investigate feature importance analysis which indicated initial concentration ratio pH most influential toward followed Pyrolysis while features like physical properties as surface area pore structure had minimal effect on efficiency. These findings highlighted using ML guiding solutions it provides efficient prediction ease selection application.

Language: Английский

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Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review

Next Materials, Journal Year: 2025, Volume and Issue: 8, P. 100522 - 100522

Published: Feb. 10, 2025

Language: Английский

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Prediction of Lithofacies in Heterogeneous Shale Reservoirs Based on a Robust Stacking Machine Learning Model

Minerals, Journal Year: 2025, Volume and Issue: 15(3), P. 240 - 240

Published: Feb. 26, 2025

The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction shale is crucial for identifying sweet spots oil gas development. However, obtaining through core sampling during drilling challenging, the accuracy traditional logging curve intersection methods insufficient. To efficiently accurately predict lithofacies, this study proposes hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting, Extreme Gradient Categorical Boosting. employs Grid Search Method to automatically search optimal hyperparameters, using classifiers base learners. predictions from these learners are then used new features, Logistic Regression serves final meta-classifier prediction. A total 3323 data points were collected six wells train test model, with performance evaluated on two blind that not involved in training process. results indicate stacking predicts achieving an Accuracy, Recall, Precision, F1 Score 0.9587, 0.959, respectively, set. This achievement provides technical support evaluation spot exploration.

Language: Английский

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Leveraging Artificial Intelligence Models (GBR, SVR, and GA) for Efficient Chromium Reduction via UV/Trichlorophenol/Sulfite Reaction

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104599 - 104599

Published: March 1, 2025

Language: Английский

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Ti3C2Tx MXene-Based Hybrid Photocatalysts in Organic Dye Degradation: A Review

Molecules, Journal Year: 2025, Volume and Issue: 30(7), P. 1463 - 1463

Published: March 26, 2025

This review provides an overview of the fabrication methods for Ti3C2Tx MXene-based hybrid photocatalysts and evaluates their role in degrading organic dye pollutants. MXene has emerged as a promising material due to its high metallic conductivity, excellent hydrophilicity, strong molecular adsorption, efficient charge transfer. These properties facilitate faster separation minimize electron–hole recombination, leading exceptional photodegradation performance, long-term stability, significant attention degradation applications. significantly improve efficiency, evidenced by higher percentage reduced time compared conventional semiconducting materials. also highlights computational techniques employed assess enhance performance degradation. It identifies challenges associated with photocatalyst research proposes potential solutions, outlining future directions address these obstacles effectively.

Language: Английский

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Metaheuristic-driven CatBoost model for accurate seepage loss prediction in lined canals

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(5)

Published: March 25, 2025

Language: Английский

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The pivotal and transformative role of artificial intelligence in advanced multidimensional modeling and optimization of complex cefixime separation processes using 3-hydroxyphenol-formaldehyde nanostructures: A multi-layered analytical approach

Microchemical Journal, Journal Year: 2025, Volume and Issue: unknown, P. 113817 - 113817

Published: April 1, 2025

Language: Английский

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