The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae
Abstract
Hydrothermal liquefaction (HTL) of algae is a promising biofuel production technology. However, it is always difficult and time-consuming to identify the best optimal conditions of HTL for different algae by the conventional experimental study. Therefore, machine learning (ML) algorithms were applied to predict and optimize bio-oil production with algae compositions and HTL conditions as inputs, and bio-oil yield (Yield_oil), and the contents of oxygen (O_oil) and nitrogen (N_oil) in bio-oil as outputs. Results indicated that gradient boosting regression (GBR, average test R 2 ∼ 0.90) exhibited better performance than random forest (RF) for both single and multi-target tasks prediction. Furthermore, the model-based interpretation suggested that the relative importance of operating conditions (temperature and residence time) was higher than algae characteristics for the three targets. Moreover, ML-based reverse and forward optimizations were implemented with experimental verifications. The verifications were acceptable, showing great potential of ML-aided HTL for producing desirable bio-oil.
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“From 2018 to 2020, the number decreased until reaching three publications, and, in 2021, five papers were published [39, 68–71].”
From Research Progress, Trends, and Future Prospects on Hydrothermal Liquefaction of Algae for Biocrude Production: A Bibliometric Analysis (2024) · cites this paper as [71]
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Machine learning predicts and optimizes hydrothermal liquefaction of biomass
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Metadata
- Authors
- Zhang, W., Li, J., Liu, T., Leng, S., Yang, L., Peng, H., Jiang, S., Zhou, W., Leng, L., & Li, H.
- Affiliations
- Central South University (CN)
- National University of Singapore (SG)
- Nanchang University (CN)
- Corresponding author
- Leng, Lijian
- Venue
- Bioresource Technology
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 342
- Pages
- 126011
- ISSN
- 0960-8524 (ISSN-L)
- OpenAlex
- W3200060725
- Cited by
- 188
- References
- 42
- Citations by year
- 2026: 39 · 2025: 36 · 2024: 39 · 2023: 41 · 2022: 30 · 2021: 3
- FWCI
- 8.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Bioresource Technology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (51808278)
- National Key Research and Development Program of China
- National Key Research and Development Program of China Stem Cell and Translational Research (2021YFE0104900)
- Topics
- Physical Sciences › Energy › Fuel Technology › Thermochemical Biomass Conversion Processes
- Physical Sciences › Energy › Fuel Technology › Biodiesel Production and Applications
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Keywords
- hydrothermal liquefaction, algae, bio-oil production, machine learning prediction, gradient boosting regression, random forest, multi-target regression, process optimization, inverse optimization, operating conditions
- MeSH terms
- Machine Learning, Plant Oils, Temperature, Water, Biomass, Biofuels, Polyphenols
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