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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A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

Environmental Pollution, Год журнала: 2023, Номер 324, С. 121363 - 121363

Опубликована: Фев. 28, 2023

Язык: Английский

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Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Bioresource Technology, Год журнала: 2023, Номер 385, С. 129444 - 129444

Опубликована: Июль 1, 2023

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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Machine learning for sustainable organic waste treatment: a critical review

npj Materials Sustainability, Год журнала: 2024, Номер 2(1)

Опубликована: Апрель 8, 2024

Abstract Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven techniques for treatment, encompassing neural networks, support vector machines, decision trees, random forests, Gaussian process regression, k -nearest neighbors. The application these explored terms their capacity complex processes. Additionally, the delves into physics-informed highlighting significance integrating domain knowledge improved model consistency. Comparative analyses are carried out to provide insights strengths weaknesses each technique, aiding practitioners selecting appropriate models diverse applications. Transfer learning specialized network variants also discussed, offering avenues enhancing predictive capabilities. work contributes valuable field modeling, emphasizing importance understanding nuances technique informed decision-making various treatment scenarios.

Язык: Английский

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Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning

Journal of Hazardous Materials, Год журнала: 2024, Номер 471, С. 134392 - 134392

Опубликована: Апрель 23, 2024

Язык: Английский

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Exploring sludge yield patterns through interpretable machine learning models in China's municipal wastewater treatment plants

Resources Conservation and Recycling, Год журнала: 2024, Номер 204, С. 107467 - 107467

Опубликована: Фев. 16, 2024

Язык: Английский

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Co-composting of dewatered sludge and wheat straw with newly isolated Xenophilus azovorans: Carbon dynamics, humification, and driving pathways

Journal of Environmental Management, Год журнала: 2024, Номер 365, С. 121613 - 121613

Опубликована: Июнь 29, 2024

Язык: Английский

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