Data and Code Repository for "Feedstock-Driven Hydrochar Design for Enhanced Sludge Biorefinery: A Meta-Analysis and Experimental Validation Study"

This repository contains the datasets and computational scripts supporting the study on feedstock-driven hydrochar design for sludge biorefinery optimization. The repository includes: A processed meta-analysis dataset compiled from published literature, containing feedstock characteristics, hydrothermal parameters, and hydrochar physicochemical properties used for Random Forest (RF) modeling and SHAP feature importance analysis. Python scripts used for: Random Forest model construction and evaluation SHAP-based feature importance analysis The meta-analysis dataset includes only processed numerical data extracted from published studies and does not contain copyrighted materials.