Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(6), P. 9121 - 9134
Published: Jan. 6, 2024
Language: Английский
Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(6), P. 9121 - 9134
Published: Jan. 6, 2024
Language: Английский
International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 54, P. 127 - 160
Published: May 21, 2023
Language: Английский
Citations
114Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 466, P. 143081 - 143081
Published: April 23, 2023
Language: Английский
Citations
47Bioresource Technology, Journal Year: 2023, Volume and Issue: 388, P. 129725 - 129725
Published: Sept. 7, 2023
Language: Английский
Citations
43Biochar, Journal Year: 2024, Volume and Issue: 6(1)
Published: June 21, 2024
Abstract Biochar, a carbon-rich material produced from biomass waste through thermal conversion, holds great environmental promise. This article offers comprehensive overview of the various feedstocks used in biochar production, different types degradation processes, characterization, properties, modifications to engineered materials, and their applications environment. The quality biochar, including surface area, pore size volume, functional group formation, is significantly influenced by specific conditions under which conversion takes place. Each diverse processes employed produce yields distinct set properties final product. In recent years, has gained widespread recognition utilization fields such as wastewater treatment, carbon sequestration, reduction greenhouse gas emissions, biogas catalysis biofuel industries, construction, soil enhancement. summary, promising mitigation tool achieve sustainable addition its benefits, application presents several challenges, selection feedstocks, methods biochar. current review summarizes factors that could lead significant advancements future applications. Graphical
Language: Английский
Citations
25Biofuels Bioproducts and Biorefining, Journal Year: 2024, Volume and Issue: 18(2), P. 567 - 593
Published: Feb. 5, 2024
Abstract Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand sustainable energy. Efficient management systems are needed in order exploit fully of biochar. Modern machine learning (ML) techniques, and particular ensemble approaches explainable AI methods, valuable forecasting properties efficiency biochar properly. Machine‐learning‐based forecasts, optimization, feature selection critical improving techniques. In this research, we explore influences these techniques on accurate yield range sources. We emphasize importance interpretability model, improves human comprehension trust ML predictions. Sensitivity analysis shown be an effective technique finding crucial characteristics that influence synthesis Precision prognostics have far‐reaching ramifications, influencing industries such logistics, technologies, successful use renewable These advances can make substantial contribution greener future encourage development circular biobased economy. This work emphasizes using sophisticated data‐driven methodologies synthesis, usher ecologically friendly energy solutions. breakthroughs hold key more environmentally future.
Language: Английский
Citations
23Sustainable Energy Technologies and Assessments, Journal Year: 2022, Volume and Issue: 55, P. 102991 - 102991
Published: Dec. 27, 2022
Language: Английский
Citations
44Chemosphere, Journal Year: 2022, Volume and Issue: 311, P. 136989 - 136989
Published: Oct. 25, 2022
Language: Английский
Citations
40Environmental Research, Journal Year: 2023, Volume and Issue: 236, P. 116770 - 116770
Published: July 28, 2023
Language: Английский
Citations
35Energy & Fuels, Journal Year: 2023, Volume and Issue: 37(22), P. 17310 - 17327
Published: Oct. 28, 2023
Biochar is found to possess a large number of applications in energy and environmental areas. However, biochar could be produced from variety sources, showing that yield proximate analysis outcomes change over wide range. Thus, developing high-accuracy machine learning-based tool very necessary predict characteristics. In this study, hybrid technique was developed by blending modern learning (ML) algorithms with cooperative game theory-based Shapley Additive exPlanations (SHAP). SHAP employed help improve interpretability while offering insights into the decision-making process. ML models, linear regression as baseline method, more advanced methodologies like AdaBoost boosted tree (BRT) were employed. The prediction models evaluated on battery statistical metrics, all observed robust enough. Among three BRT-based model delivered best performance R2 range 0.982 0.999 during training phase 0.968 0.988 test. value mean squared error also quite low (0.89 9.168) for models. quantified each input element expected results provided in-depth understanding underlying dynamics. helped reveal temperature main factor affecting response predictions. proposed here provides substantial manufacturing process, allowing improved control properties increasing use sustainable flexible material numerous applications.
Language: Английский
Citations
31International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 49, P. 868 - 909
Published: Sept. 29, 2023
Language: Английский
Citations
29