Predicting harvesting efficiency of microalgae with magnetic nanoparticles using machine learning models
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A ML approach predicted harvesting efficiency of magnetic nanoparticles for microalgae flocculation based on 1151 data points, with optimal XGBoost achieving R2 = 0.932.
“For example, a ML approach was used to predict the harvesting efficiency (HE) of magnetic nanoparticles (MNPs) for microalgae flocculation based on 1151 data points incorporating nanoparticle properties, microalgal traits, and operational conditions. The optimal XGBoost model achieved strong predictive performance (R2 = 0.932), and SHAP analysis further revealed key factors influencing HE, thus”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [45]
Cites
Deconinck, N., Muylaert, K., Ivens, W., & Vandamme, D. (2018) · Algal Research · doi:10.1016/j.algal.2018.01.016 · Cited by 60 · see citing works
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Wu, Y., Shan, L., Zhao, W., & Lu, X. (2025) · Marine Drugs · doi:10.3390/md23050184 · Cited by 59 · see citing works
- Authors
- Fu, Y., Zhang, Q., Tan, Z., Yu, S., & Zhang, Y.
- Venue
- Journal of Environmental Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 13
- Issue
- 2
- Pages
- 115406
- ISSN
- 2213-3437
- Cited by
- 13
- References
- 1
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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