A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization
The advent of novel waste disposal methodologies, which are energy-efficient and environmentally benign, has created opportunities for the deployment of artificial intelligence technologies in the management of solid waste treatment. This review examines the deployment of AI-optimized control algori...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Green Energy and Resources |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949720525000190 |
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| author | Dongjie Pang Cristina Moliner Tao Wang Jin Sun Xinyan Zhang Yingping Pang Xiqiang Zhao Zhanlong Song Ziliang Wang Yanpeng Mao Wenlong Wang |
| author_facet | Dongjie Pang Cristina Moliner Tao Wang Jin Sun Xinyan Zhang Yingping Pang Xiqiang Zhao Zhanlong Song Ziliang Wang Yanpeng Mao Wenlong Wang |
| author_sort | Dongjie Pang |
| collection | DOAJ |
| description | The advent of novel waste disposal methodologies, which are energy-efficient and environmentally benign, has created opportunities for the deployment of artificial intelligence technologies in the management of solid waste treatment. This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. The implementation of AI-based solutions enables the optimization of key characteristics, such as temperature and oxygen levels, with the objective of achieving optimal energy efficiency while minimizing the emission of harmful substances, including CO, NOx, and dioxins. Notwithstanding these advancements, challenges remain in hyperparameter tuning, probabilistic assessments, and feature generation. A comprehensive understanding of future technologies will necessitate a synthesis of knowledge and data-oriented approaches, the design of autonomous control systems, and the integration of digital twin technologies to bridge the gap between theory and practice. |
| format | Article |
| id | doaj-art-b62dd72772ea49e9a5819f13bfbfe4a0 |
| institution | DOAJ |
| issn | 2949-7205 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Green Energy and Resources |
| spelling | doaj-art-b62dd72772ea49e9a5819f13bfbfe4a02025-08-20T03:09:07ZengElsevierGreen Energy and Resources2949-72052025-06-013210013210.1016/j.gerr.2025.100132A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimizationDongjie Pang0Cristina Moliner1Tao Wang2Jin Sun3Xinyan Zhang4Yingping Pang5Xiqiang Zhao6Zhanlong Song7Ziliang Wang8Yanpeng Mao9Wenlong Wang10National Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaDepartment of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, Genoa, 16145, ItalyNational Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaNational Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaNational Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaNational Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaNational Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaNational Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaNational Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, China; Corresponding author.National Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, China; Corresponding author.National Engineering Laboratory for Reducing Emissions from Coal Combustion, Shandong University, Jinan, 250061, ChinaThe advent of novel waste disposal methodologies, which are energy-efficient and environmentally benign, has created opportunities for the deployment of artificial intelligence technologies in the management of solid waste treatment. This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. The implementation of AI-based solutions enables the optimization of key characteristics, such as temperature and oxygen levels, with the objective of achieving optimal energy efficiency while minimizing the emission of harmful substances, including CO, NOx, and dioxins. Notwithstanding these advancements, challenges remain in hyperparameter tuning, probabilistic assessments, and feature generation. A comprehensive understanding of future technologies will necessitate a synthesis of knowledge and data-oriented approaches, the design of autonomous control systems, and the integration of digital twin technologies to bridge the gap between theory and practice.http://www.sciencedirect.com/science/article/pii/S2949720525000190Artificial intelligenceSolid waste treatmentEnergy recovery optimizationEmission controlMachine learning algorithms |
| spellingShingle | Dongjie Pang Cristina Moliner Tao Wang Jin Sun Xinyan Zhang Yingping Pang Xiqiang Zhao Zhanlong Song Ziliang Wang Yanpeng Mao Wenlong Wang A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization Green Energy and Resources Artificial intelligence Solid waste treatment Energy recovery optimization Emission control Machine learning algorithms |
| title | A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization |
| title_full | A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization |
| title_fullStr | A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization |
| title_full_unstemmed | A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization |
| title_short | A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization |
| title_sort | mini review on ai driven thermal treatment of solid waste emission control and process optimization |
| topic | Artificial intelligence Solid waste treatment Energy recovery optimization Emission control Machine learning algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2949720525000190 |
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