Application of Machine Learning Algorithms to Predict Composting Process Performance

Four machine learning models (Decision Tree Regressor, Linear Regression, XGBoost Regression, K-Neighbors Regressor) were developed to predict the outcomes of a composting process based on key input parameters, including Ambient Temperature, mixture composition, and initial feedstock volume. The models were trained on data from 88 composting batches, monitoring temperature evolution, and compost yield. Performance evaluation demonstrated high accuracy in predicting compost maturity, process duration, and final product quantity. These predictive models could optimize composting operations by enabling real-time adjustments, improving efficiency, and enhancing resource management in sustainable waste processing.