Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass

Nitrogen heterocyclic compounds in bio-oil (NH_Oil) made from biomass pyrolysis such as pyrroles, pyrazines, and indoles, have a relative content of 0-30%. NH_Oil is a NOx precursor if bio-oil is used as a fuel, but it has a high potential as a precursor for high-value chemicals. However, predicting and controlling NH_Oil are challenging because of the complexity of the pyrolysis reaction system. Machine learning (ML) shows significant potential for addressing this issue. In this study, the relative contents of NH_Oil, 5-membered NH_Oil, 6membered NH_Oil, bio-oil yield, and the content of nitrogen in bio-oil were predicted using Random Forest and gradient boosting regression algorithms, with test regression coefficients values of 0.77-0.87 and 0.74-0.81 obtained for the former and latter ML models, respectively. Biomass N was the most important factor in predicting the bio-oil yield, whereas biomass N/C was the most significant of the other four targets and can be used as a proxy to assess the potential of biomass feedstock as fuel material or N-containing value-added chemical precursor. The optimization of pyrolysis parameters within ML models provides useful information for instructing experimental studies, indicating ML-aided bio-oil prediction and engineering show great promise and are worthy of further investigation.

Automated summary

Machine learning algorithms were used to predict and control the relative content of nitrogen heterocyclic compounds in bio-oil (NH_Oil) produced from biomass pyrolysis. The results showed that machine learning has significant potential in addressing this issue and guiding experimental studies.