Conversion of organic solid waste into energy and functional materials using biochar catalyst: Bibliometric analysis, research progress, and directions

Applied Catalysis B Environment and Energy, Journal Year: 2023, Volume and Issue: 340, P. 123223 - 123223

Published: Sept. 1, 2023

Language: Английский

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Machine learning applications for biochar studies: A mini-review

Bioresource Technology, Journal Year: 2024, Volume and Issue: 394, P. 130291 - 130291

Published: Jan. 4, 2024

Language: Английский

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A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste

Carbon Neutrality, Journal Year: 2024, Volume and Issue: 3(1)

Published: Jan. 8, 2024

Abstract The utilization of biochar derived from biomass residue to enhance anaerobic digestion (AD) for bioenergy recovery offers a sustainable approach advance energy and mitigate climate change. However, conducting comprehensive research on the optimal conditions AD experiments with addition poses challenge due diverse experimental objectives. Machine learning (ML) has demonstrated its effectiveness in addressing this issue. Therefore, it is essential provide an overview current ML-optimized processes biochar-enhanced order facilitate more systematic ML tools. This review comprehensively examines material flow preparation impact comprehension reviewed optimize production process perspective. Specifically, summarizes application techniques, based artificial intelligence, predicting yield properties residues, as well their AD. Overall, analysis address challenges recovery. In future research, crucial tackle that hinder implementation pilot-scale reactors. It recommended further investigate correlation between physicochemical process. Additionally, enhancing role throughout entire holds promise achieving economically environmentally optimized efficiency. Graphical

Language: Английский

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Rethinking the biochar impact on the anaerobic digestion of food waste in bench-scale digester: Spatial distribution and biogas production

Bioresource Technology, Journal Year: 2025, Volume and Issue: unknown, P. 132115 - 132115

Published: Jan. 1, 2025

Language: Английский

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Biochar production and its environmental applications: Recent developments and machine learning insights

Bioresource Technology, Journal Year: 2023, Volume and Issue: 387, P. 129634 - 129634

Published: Aug. 21, 2023

Language: Английский

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Using automated machine learning techniques to explore key factors in anaerobic digestion: At the environmental factor, microorganisms and system levels

Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 475, P. 146069 - 146069

Published: Sept. 15, 2023

Language: Английский

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Machine learning in clarifying complex relationships: Biochar preparation procedures and capacitance characteristics

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 485, P. 149975 - 149975

Published: Feb. 24, 2024

Language: Английский

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Explicable Machine Learning for Predicting High-Efficiency Lignocellulose Pretreatment Solvents Based on Kamlet–Taft and Polarity Parameters

ACS Sustainable Chemistry & Engineering, Journal Year: 2024, Volume and Issue: 12(19), P. 7578 - 7590

Published: April 29, 2024

Incorporating density functional theory (DFT) and machine learning (ML) methodologies, an intrinsic relationship model was developed utilizing the Kamlet–Taft parameters polarity values of 104 deep eutectic solvents (DES). DES with high lignocellulosic pretreatment efficiency were expected to be screened through synergistic combination hydrogen bond acidity (α), basicity (β), polarization (Π*) molecular index (MPI). Partial least-squares (PLS) models a variety ML used predict cellulose retention delignification. The XGBoost has highest predictive performance R2 0.97 0.91, respectively. Feature importance analysis partial dependence explain variables based on model. showed that α, β, Π* MPI donor determined efficiency. among 4 is nonlinear, there are multiple extreme in different intervals. gave parameter range corresponding Based given this study, composition ratio can selected ensure at least 80% retained 50% lignin removed. Molecular simulation results these highly efficient often contain large number bonds polar groups.

Language: Английский

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Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation

Bioresource Technology, Journal Year: 2024, Volume and Issue: 399, P. 130624 - 130624

Published: March 21, 2024

Language: Английский

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Applications of machine learning tools for biological treatment of organic wastes: Perspectives and challenges

Circular Economy, Journal Year: 2024, Volume and Issue: 3(2), P. 100088 - 100088

Published: May 31, 2024

Biological treatment technologies (such as anaerobic digestion, composting, and insect farming) have been extensively employed to handle various degradable organic wastes. However, the inherent complexity instability of biological processes adversely affect production renewable energy nutrient-rich products. To ensure stable consistent product quality, researchers invested heavily in control strategies for treatment, with machine learning (ML) recently proving effective optimizing predicting parameters, detecting disturbances, enabling real-time monitoring. This review critically assesses application ML providing an in-depth evaluation key algorithms. study reveals that artificial neural networks, tree-based models, support vector machines, genetic algorithms are leading treatment. A thorough investigation applications farming underscores its remarkable capacity predict products, optimize processes, perform monitoring, mitigate pollution emissions. Furthermore, this outlines challenges prospects encountered applying highlighting crucial directions future research area.

Language: Английский

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