Machine learning for hydrothermal treatment of biomass: A review

[Display omitted] •Biomass compositions and hydrothermal treatment (HTT) parameters predict products.•Machine learning (ML) models and procedure were introduced and compared.•ML can predict yield, compositions, and properties of products from HTT accurately.•ML-aided prediction, optimization, and ap...

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Bibliographic Details
Published in:Bioresource technology 2023-02, Vol.370, p.128547-128547, Article 128547
Main Authors: Zhang, Weijin, Chen, Qingyue, Chen, Jiefeng, Xu, Donghai, Zhan, Hao, Peng, Haoyi, Pan, Jian, Vlaskin, Mikhail, Leng, Lijian, Li, Hailong
Format: Article
Language:English
Subjects:
Biofuels
Biomass
Hydrolases
Hydrothermal carbonization
Hydrothermal gasification
Hydrothermal liquefaction
Machine learning
Temperature
Water
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Summary:[Display omitted] •Biomass compositions and hydrothermal treatment (HTT) parameters predict products.•Machine learning (ML) models and procedure were introduced and compared.•ML can predict yield, compositions, and properties of products from HTT accurately.•ML-aided prediction, optimization, and application are promising in HTT field. Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.
ISSN:0960-8524
1873-2976
DOI:10.1016/j.biortech.2022.128547