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Machine learning framework for intelligent prediction of compost maturity towards automation of food waste composting system

Abstract:

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 R<sup>2</sup> 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.