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Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction,
Li-Ting Huang1, Jia-Yi Hou2, Hong-Tao Liu2
1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.
Waste Management (New York, N.Y.)
|February 24, 2024
Summary
Machine learning (ML) enhances aerobic composting of organic solid waste (OSW) by improving simulation accuracy and optimizing product quality. This review highlights ML
Area of Science:
- Environmental Science
- Biotechnology
- Data Science
Background:
- Aerobic composting treats organic solid waste (OSW), producing fertilizers but facing challenges in simulation and optimization due to complex metabolism.
- Machine learning (ML) shows promise for predicting and optimizing OSW composting parameters and product quality.
Conclusions:
- ML is a versatile tool for enhancing aerobic composting efficiency and product quality.
- Further research can leverage advanced ML models for more objective and accurate OSW composting management.
