A research team from Tongji University has published a review in Circular Economy that explores the application of machine learning (ML) in the biological treatment of organic wastes. The article examines how various ML algorithms can enhance processes such as composting, anaerobic digestion, and insect farming, aiming to improve treatment efficiency and product quality.
artificial neural networks; RF: random forest; GBR: gradient boosting regression; SVM: support vector machine; GA: genetic algorithm. Image Credit: Circular Economy, Tsinghua University Press
Anaerobic digestion, composting, and insect farming are key biological treatment techniques for managing organic waste and converting it into valuable products like biogas and organic fertilizers. Due to their inherent complexity and instability, these processes often face challenges that can reduce productivity and the quality of the final products, and traditional control strategies often fall short in addressing these issues effectively.
To enhance performance and sustainability, cutting-edge techniques such as machine learning (ML) are being explored to improve the prediction, optimization, and monitoring of these biological treatments.
The review article thus focuses on several key ML algorithms, including artificial neural networks, tree-based models, support vector machines, and genetic algorithms, providing a comprehensive assessment of how these techniques can be applied to biological treatment processes. Specifically, ML can help predict treatment outcomes with greater accuracy, optimize process parameters, and enable real-time monitoring.
This can lead to substantial improvements in the stability and efficiency of processes like composting, anaerobic digestion, and insect farming. For example, ML models have demonstrated effectiveness in predicting biogas production, assessing compost maturity, and optimizing conditions for insect farming.
The paper also addresses challenges associated with ML applications, such as model selection, parameter tuning, and the need for practical engineering validation. Despite these obstacles, ML holds significant potential to revolutionize biological waste treatment, enhancing its efficiency, reliability, and sustainability.
ML offers unprecedented opportunities to enhance the efficiency and stability of biological treatment processes. By leveraging advanced algorithms, we can better predict and optimize these complex systems, ultimately contributing to more sustainable waste management solutions.
Dr. Fan Lü, Study Corresponding Author, Tongji University
Machine learning holds significant promise for advancing waste management techniques in biological treatment. By optimizing processes and ensuring consistent product quality, ML can mitigate environmental impacts and enhance resource recovery. To fully unlock ML's potential in this field, future research should focus on addressing current challenges, such as improving model explainability and conducting practical engineering validations.
This study is supported by funding from the National Natural Science Foundation of China and the Shanghai Science and Technology Innovation Action Plan International Science and Technology Cooperation Program.
Journal Reference:
Chen, L., et al. (2024) Applications of machine learning tools for biological treatment of organic wastes: Perspectives and challenges. Circular Economy.doi.org/10.1016/j.cec.2024.100088