期刊
BIORESOURCE TECHNOLOGY
卷 358, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2022.127348
关键词
Hydrothermal treatment; Aqueous phase; Machine learning; Wastewater properties; Biomass
资金
- National Key Research and Devel-opment Program of China [2021YFE0104900]
- Science and Tech-nology Innovation Program of Hunan Province [2021RC4005]
- Fundamental Research Funds for the Central Universities of Central South University [2022ZZTS0520]
- National Natural ScienceFoundation of China [51906247]
Hydrothermal treatment is a potential technology for producing biofuel from wet biomass, but the properties of the generated aqueous phase are not well-studied. In this study, machine learning models were developed to predict the properties of the aqueous phase based on biomass feedstock and hydrothermal treatment parameters. The results showed that the gradient boosting decision tree can accurately predict the properties, and the feature importance analysis provided new insights.
Hydrothermal treatment (HTT) is a potential technology for producing biofuel from wet biomass. However, the aqueous phase (AP) is generated inevitably in the process of HTT, and studies are lacking on the detailed exploration of AP properties. Therefore, machine learning (ML) models were built for predicting the pH, total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) of the AP based on biomass feedstock and HTT parameters. Results showed that the gradient boosting decision tree (average testing R-2 0.85-0.96) can accurately predict the above wastewater properties for both single-and multi-target models. ML-based feature importance indicated that nitrogen content of biomass, solid content, and temperature were the top three critical features for pH, TN, and TP, while those for TOC were reaction time, lipid, and temperature. This ML approach provides new insights to understand the formation and properties of the HTT AP by ML rather than timeconsuming experiments.
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