Robust Ensemble-Based Model and Web Application for Nitrogen Content Prediction in Hydrochar from Sewage Sludge

Hydrochar is a carbon-rich material produced through the hydrothermal carbonization (HTC) of wet biomass such as sewage sludge. Its nitrogen content is a critical quality parameter, influencing its suitability for use as a soil amendment and its potential environmental impacts. This study develops a high-accuracy ensemble machine learning framework to predict the nitrogen content of hydrochar derived from sewage sludge based on feedstock compositions and HTC process conditions. Four ensemble algorithms—Gradient Boosting Regression Trees (GBRTs), AdaBoost, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)—were trained using an 80/20 train–test split and evaluated through standard statistical metrics. GBRT and XGBoost provided the best performance, achieving R2 values of 0.993 and 0.989 and RMSE values of 0.169 and 0.213 during training, while maintaining strong predictive capabilities on the test dataset. SHAP analyses identified nitrogen content, ash content, and heating temperature as the most influential predictors of hydrochar nitrogen levels. Predicting nitrogen behaviour during HTC is environmentally relevant, as the improper management of nitrogen-rich hydrochar residues can contribute to nitrogen leaching, eutrophication, and disruption of aquatic biogeochemical cycles. The proposed ensemble-based modelling approach therefore offers a reliable tool for optimizing HTC operations, supporting sustainable sludge valorisation, and reducing environmental risks associated with nitrogen emissions.