Artificial intelligence and machine learning approaches in composting process: A review

Bioresource Technology, Год журнала: 2023, Номер 370, С. 128539 - 128539

Опубликована: Янв. 3, 2023

Язык: Английский

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Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Bioresource Technology, Год журнала: 2022, Номер 369, С. 128445 - 128445

Опубликована: Дек. 5, 2022

Язык: Английский

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Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioresource Technology, Год журнала: 2022, Номер 369, С. 128451 - 128451

Опубликована: Дек. 9, 2022

Язык: Английский

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Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges

Waste Management, Год журнала: 2024, Номер 178, С. 155 - 167

Опубликована: Фев. 24, 2024

Язык: Английский

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Biodegradation of high di-(2-Ethylhexyl) phthalate (DEHP) concentration by food waste composting and its toxicity assessment using seed germination test

Environmental Pollution, Год журнала: 2022, Номер 316, С. 120640 - 120640

Опубликована: Ноя. 17, 2022

Язык: Английский

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MachIne learning for nutrient recovery in the smart city circular economy – A review

Process Safety and Environmental Protection, Год журнала: 2023, Номер 173, С. 529 - 557

Опубликована: Март 16, 2023

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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Review: Biotic and abiotic approaches to artificial humic acids production

Renewable and Sustainable Energy Reviews, Год журнала: 2023, Номер 187, С. 113771 - 113771

Опубликована: Сен. 23, 2023

Язык: Английский

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Effect of different bulking agents on fed-batch composting and microbial community profile

Environmental Research, Год журнала: 2024, Номер 249, С. 118449 - 118449

Опубликована: Фев. 13, 2024

Язык: Английский

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Cotton Irrigation Scheduling: Which Approach is the Best Fit for Georgia?

˜The œjournal of cotton science/Journal of cotton science, Год журнала: 2025, Номер 28(3), С. 136 - 144

Опубликована: Янв. 27, 2025

Cotton (Gossypium hirsutum) is one of the most difficult crops to manage irrigation effectively due crop’s perennial physiology. In recent years, many new technologies have been developed help improve management. The main objective this study was evaluate various management tools and assist farmers in determining which method best for their operation. Other objectives included monitoring soil moisture optimal application point each by logging total rainfall distribution throughout growing season. A three-year conducted at University Georgia (UGA) Stripling Irrigation Research Park near Camilla, GA where cotton grown on loamy sand soil. lateral movement, overhead sprinkler system equipped with a variable rate allowed plots be irrigated independently based treatment. treatments 20- 45-kPa weighted average water tension (SWT) measurements made using three Watermark SWT sensors placed two replicates. UGA SmartIrrigation app (SI app), Checkbook method, rainfed check were trial. Each evaluated crop yield, water-use efficiency, profitability. analysis revealed significant variations several metrics between validates threshold SI are top-performing advanced scheduling showed importance strengths weaknesses method.

Язык: Английский

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