Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model. (September 2022)

• Record Type: Journal Article
• Authors: Ding, Shang; Huang, Wuji; Xu, Weijian; Wu, Yiqu; Zhao, Yuxiang; Fang, Ping; Hu, Baolan; Lou, Liping
• Is Part Of: Bioresource technology. Volume 360(2022)
• Publication Date: 2022-09
• Subjects: Kitchen waste -- Aerobic composting -- Interpretive analysis -- Machine learning; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.127606
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Composting maturity could be predicted by using the machine learning model. The key parameters of the composting process were determined by SHAP analysis. The optimal range of processing parameters was quantified by PDA. The model-based optimization strategies could improve composting maturity. Abstract: As a novel analytical method based on big data, machine learning model can explore the relationship between different parameters and draw universal conclusions, which was used to predict composting maturity and identify key parameters in this study. The results showed that the Stacking model exhibited excellent prediction accuracy. SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) were performed to evaluate the importance of different parameters as well as their optimal interval. Optimal starting conditions should be maintained in the mesophilic state (temperature: 30-45℃, moisture content: 55–65%, pH: 6.3–8.0), and nutrients (total nitrogen > 2.3%, total organic carbon > 35%) should be adjusted in the thermophilic state. Experiments revealed that model-based optimization strategies could improve composting maturity because they could optimize compost microbial flora and perform complex carbon cycle functions. In conclusion, this study provides new insights into the enhancement of the composting process.