Closing the loop: technological innovations in food waste valorisation for global sustainability

Discover Sustainability, Год журнала: 2025, Номер 6(1)

Опубликована: Апрель 8, 2025

Язык: Английский

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Machine learning prediction of delignification and lignin structure regulation of deep eutectic solvents pretreatment processes

Industrial Crops and Products, Год журнала: 2023, Номер 203, С. 117138 - 117138

Опубликована: Июль 31, 2023

Язык: Английский

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The new strategy of using humic acid loaded biochar to enhance the anaerobic digestion of cow manure for methane production

Journal of Cleaner Production, Год журнала: 2023, Номер 428, С. 139353 - 139353

Опубликована: Окт. 17, 2023

Язык: Английский

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Prediction of heavy metal removal performance of sulfate-reducing bacteria using machine learning

Bioresource Technology, Год журнала: 2024, Номер 397, С. 130501 - 130501

Опубликована: Фев. 27, 2024

Язык: Английский

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Machine learning-based prediction of heavy metal immobilization rate in the solidification/stabilization of municipal solid waste incineration fly ash (MSWIFA) by geopolymers

Journal of Hazardous Materials, Год журнала: 2024, Номер 467, С. 133682 - 133682

Опубликована: Фев. 2, 2024

Язык: Английский

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Machine learning-based exploration of biochar for environmental management and remediation

Journal of Environmental Management, Год журнала: 2024, Номер 360, С. 121162 - 121162

Опубликована: Май 14, 2024

Язык: Английский

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

Circular Economy, Год журнала: 2024, Номер 3(2), С. 100088 - 100088

Опубликована: Май 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.

Язык: Английский

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Production capacity prediction based response conditions optimization of straw reforming using attention-enhanced convolutional LSTM integrating data expansion

Applied Energy, Год журнала: 2024, Номер 365, С. 123253 - 123253

Опубликована: Апрель 17, 2024

Язык: Английский

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A data-driven approach for revealing the linkages between differences in electrochemical properties of biochar during anaerobic digestion using automated machine learning

The Science of The Total Environment, Год журнала: 2024, Номер 927, С. 172291 - 172291

Опубликована: Апрель 7, 2024

Язык: Английский

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Ruminal content biochar supplementation for enhanced biomethanation of rice straw: Focusing on biochar characterization and dose optimization

The Science of The Total Environment, Год журнала: 2023, Номер 905, С. 167250 - 167250

Опубликована: Сен. 21, 2023

Язык: Английский

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