Optimizing Process Conditions for H2 Production from Biomass and Plastic Waste Gasification using Machine Learning and Genetic Algorithm

Abstract

This study developed machine learning models using a dataset consisting of 963 entries with 24 variables both numerical and categorical to predict H2 production of plastic waste and biomass gasification. The models were utilized to optimize feedstock and process conditions through genetic algorithm to maximize hydrogen production.