Intelligent approaches for sustainable management and valorisation of food waste

Bioresource Technology, Journal Year: 2023, Volume and Issue: 377, P. 128952 - 128952

Published: March 24, 2023

Language: Английский

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

Bioresource Technology, Journal Year: 2023, Volume and Issue: 370, P. 128539 - 128539

Published: Jan. 3, 2023

Language: Английский

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Applicability and limitation of compost maturity evaluation indicators: A review

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 489, P. 151386 - 151386

Published: April 17, 2024

Language: Английский

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

Bioresource Technology, Journal Year: 2023, Volume and Issue: 385, P. 129444 - 129444

Published: July 1, 2023

Language: Английский

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

Waste Management, Journal Year: 2024, Volume and Issue: 178, P. 155 - 167

Published: Feb. 24, 2024

Language: Английский

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

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 471, P. 134392 - 134392

Published: April 23, 2024

Language: Английский

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Predicting maturity and identifying key factors in organic waste composting using machine learning models

Bioresource Technology, Journal Year: 2024, Volume and Issue: 400, P. 130663 - 130663

Published: April 6, 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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A machine learning framework for intelligent prediction of ash fusion temperature characteristics

Fuel, Journal Year: 2024, Volume and Issue: 362, P. 130799 - 130799

Published: Jan. 3, 2024

Language: Английский

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Functional link hybrid artificial neural network for predicting continuous biohydrogen production in dynamic membrane bioreactor

Bioresource Technology, Journal Year: 2024, Volume and Issue: 397, P. 130496 - 130496

Published: Feb. 24, 2024

Language: Английский

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