Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Bioresource technology

Reactive composting is a promising technology for recovering valuable resources from food waste, while its manual regulation is laborious and time-consuming. In this study, machine learning (ML) technologies are adopted to enable automated composting by predicting compost maturity and providing process regulation. Four machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Multilayer Perceptron (MLP) are employed to predict the seed germination index (GI) and C/N ratio. Based on the best fusion model with the highest R2 of 0.977 and 0.986 for the multi-task prediction of GI and C/N ratio, the critical factors and their interactions with maturity are identified. Moreover, the ML model is validated on a composting reactor and the ML-based prediction application can provide regulation to ensure food waste decompose within the required time. In conclusion, this compost maturity prediction system automates the reactive composting, thus reducing labor costs.

Wan Xin, Li Jie, Xie Li, Wei Zimin, Wu Junqiu, Wah Tong Yen, Wang Xiaonan, He Yiliang, Zhang Jingxin


Engineering application, Maturity prediction, Process regulation, Reactive composting