Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions

It is crucial to predict the characteristics of pyrolytic bio-oil accurately for its application, but the prediction results are greatly influenced by biomass compositions and pyrolysis conditions. In this work, different biomass compositions analysis (chemical compositions, ultimate and proximate analysis) and pyrolysis conditions (particle size, heating rate and pyrolysis temperature) were successfully used as input to analyze the characteristics of bio-oil by machine learning method. The model based on ultimate analysis is better for regression analysis of the yield, viscosity and oxygen-carbon ratio (O/C) of bio-oil. The model based on chemical compositions is better for regression analysis of calorific value and hydrogen-carbon ratio (H/C) of bio-oil. Moreover, relative error analysis and scatter diagrams were used to analyze the predicted results. In addition, the analysis of partial dependence diagram shows the influence of various factors and the interactions on the target variables. This study provides feasible thinking for the prediction of the characteristics of bio-oil obtained by biomass with different compositions under different pyrolysis conditions.

Automated summary

This study utilized different biomass compositions and pyrolysis conditions to predict the characteristics of bio-oil accurately. It found that ultimate analysis is better for predicting yield, viscosity, and oxygen-carbon ratio, while chemical compositions analysis is better for predicting calorific value and hydrogen-carbon ratio. The research provides important insights for predicting the characteristics of bio-oil obtained from biomass with different compositions under various pyrolysis conditions.