Application of machine learning in optimizing thermochemical conversion processes with pre-treatment to get higher bio-oil yield from biomass waste

Abstract

Improving the bio-oil yield is a challenging part in the thermochemical conversion processes of biomass. Implementing suitable pre-treatment technology to improve the biomass characteristics is an effective technique to increase the yield. In this study, a multi-variate random forest algorithm has been used to optimize the pre-treatment method in order to improve the biomass characteristics. The data collected from many previous studies are analysed to identify the importance of biomass characteristics in bio-oil yield. The correlation between biomass characteristics and bio-oil yield, is analysed using Pearson method and the important influencing parameters %C and %H have a very good positive correlation with a coefficient value range 0.455 to 0.818. Among the six pre-treatment methods analysed, thermochemical pre-treatment method was found effective with more than 95% improvement of many biomass characteristics. The range of voting given to the parameters identify %H be the important characteristic to be optimized first. The suggested method is validated by laboratory experiments and % accuracy between predicted and calculated biomass characteristic values showed more than 90% accuracy for all the biomass characteristic parameters tested in this study.