Prediction of product yields from lignocellulosic biomass pyrolysis based on gaussian process regression

The physicochemical characteristics of biomass, as well as the operational conditions of pyrolysis, are crucial factors that influence the production yield of tri-phase products during the pyrolysis of lignocellulosic biomass. Gaussian process regression (GPR) algorithms, being significant machine learning algorithms, are utilized for the prediction of pyrolytic product yields. These algorithms take into account the influence of biomass characteristics and pyrolysis conditions in a comprehensive manner. All of the prediction models exhibit strong performance, with R2 values exceeding 0.90 and RMSE values below 0.18. The study employed the Partial Dependence Plot (PDP) method to assess the influence patterns of individual features or interactions between two factors on the pyrolysis products. The findings indicate that the ultimate temperature reached during the pyrolysis process is a critical factor in influencing the generation of gaseous byproducts and solid residues. Specifically, higher temperatures are associated with increased production of gaseous byproducts and decreased production of solid residues. The optimal liquid phase yield was achieved at temperatures between 500 degrees C and 650 degrees C. In this research, the Particle Swarm Optimization (PSO) algorithm was employed to predict the optimal yields of pyrolysis products. This study offers a novel perspective on predicting product yields from the pyrolysis of lignocellulosic biomass with varying biomass characteristics and under different operational conditions.