Modelling hydrogen production from biomass pyrolysis for energy systems using machine learning techniques

Abstract

In the context of Industry 4.0, hydrogen gas is becoming more significant to energy feedstocks in the world. The current work researches a novel artificial smart model for characterising hydrogen gas production (HGP) from biomass composition and the pyrolysis process based on an intriguing approach that uses support vector machines (SVMs) in conjunction with the artificial bee colony (ABC) optimiser. The main results are the significance of each physico-chemical parameter on the hydrogen gas production through innovative modelling and the foretelling of the HGP. Additionally, when this novel technique was employed on the observed dataset, a coefficient of determination and correlation coefficient equal to 0.9464 and 0.9751 were reached for the HGP estimate, respectively. The correspondence between observed data and the ABC/SVM-relied approximation showed the suitable effectiveness of this procedure.