Mass transfer in heterogeneous biofilms: Key issues in biofilm reactors and AI-driven performance prediction

Biofilm reactors, known for utilizing biofilm formation for cell immobilization, offer enhanced biomass concentration and operational stability over traditional planktonic systems. However, the dense nature of biofilms poses challenges for substrate accessibility to cells and the efficient release o...

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Veröffentlicht in:Environmental science and ecotechnology 2024-11, Vol.22, p.100480, Article 100480
Hauptverfasser: Chen, Huize, Xia, Ao, Yan, Huchao, Huang, Yun, Zhu, Xianqing, Zhu, Xun, Liao, Qiang
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Sprache:eng
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Zusammenfassung:Biofilm reactors, known for utilizing biofilm formation for cell immobilization, offer enhanced biomass concentration and operational stability over traditional planktonic systems. However, the dense nature of biofilms poses challenges for substrate accessibility to cells and the efficient release of products, making mass transfer efficiency a critical issue in these systems. Recent advancements have unveiled the intricate, heterogeneous architecture of biofilms, contradicting the earlier view of them as uniform, porous structures with consistent mass transfer properties. In this review, we explore six biofilm reactor configurations and their potential combinations, emphasizing how the spatial arrangement of biofilms within reactors influences mass transfer efficiency and overall reactor performance. Furthermore, we discuss how to apply artificial intelligence in processing biofilm measurement data and predicting reactor performance. This review highlights the role of biofilm reactors in environmental and energy sectors, paving the way for future innovations in biofilm-based technologies and their broader applications. [Display omitted] •Biofilm heterogeneity impacts mass transfer and reactor efficiency.•Wrinkles and channels boost mass transfer within biofilms.•Advanced techniques reveal biofilm microstructure complexities.•Biofilm arrangement affects mass transfer in reactors.•AI predicts reactor performance without complex modeling.
ISSN:2666-4984
2096-9643
2666-4984
DOI:10.1016/j.ese.2024.100480