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

Lijian Leng, Tanghao Li, Hao Zhan, Muhammad Rizwan, Weijin Zhang, Haoyi Peng, Zequn Yang and Hailong Li

Energy, 2023, vol. 278, issue PB

Abstract: 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, 6-membered 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.

Keywords: Machine learning; Nitrogen-containing heterocyclics; Protein biomass; Pyrolysis; Bio-crude oil; Random forest (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223013610

DOI: 10.1016/j.energy.2023.127967

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