BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

Abstract

This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe₂O₃-based ฺBCLpro combining steam gasification for H₂ production. Aspen Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model. A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H₂ yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. Findings highlight the CB algorithm's superior performance, achieving up to 98% predictive accuracy, with carbon content, reducer temperature, and Fe₂O₃/Al₂O₃ mass ratio identified as crucial features. The algorithm has been developed into a user-friendly tool, BCLH2Pro, accessible via a web server. This tool is designed to assist in reducing costs, optimizing biomass selection, and planning operational conditions to maximize H₂ yield in BCLpro systems. Access to the tool can be obtained through the following link: http://bclh2pro.pythonanywhere.com/.