Towards the integration of mechanistic, microbial, and machine learning approaches in anaerobic digestion modelling and control: a review of current progress and implementation strategies.
- DOI
- 10.1016/j.biortech.2026.135967
- Published
- 2026 Sep 27
- Container
- Bioresource technology
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.biortech.2026.135967,
title = {Towards the integration of mechanistic, microbial, and machine learning approaches in anaerobic digestion modelling and control: a review of current progress and implementation strategies.},
author = {Murali R and Dekhici B and Du B and Guo B and Chen T and Zhang D and Short M},
year = {2026},
journal = {Bioresource technology},
doi = {10.1016/j.biortech.2026.135967},
url = {https://doi.org/10.1016/j.biortech.2026.135967}
}RIS
TY - JOUR TI - Towards the integration of mechanistic, microbial, and machine learning approaches in anaerobic digestion modelling and control: a review of current progress and implementation strategies. AU - Murali R AU - Dekhici B AU - Du B AU - Guo B AU - Chen T AU - Zhang D AU - Short M PY - 2026 JO - Bioresource technology DO - 10.1016/j.biortech.2026.135967 UR - https://doi.org/10.1016/j.biortech.2026.135967 ER -
APA
R, M., B, D., B, D., B, G., T, C., D, Z., & M, S. (2026). Towards the integration of mechanistic, microbial, and machine learning approaches in anaerobic digestion modelling and control: a review of current progress and implementation strategies.. Bioresource technology. https://doi.org/10.1016/j.biortech.2026.135967
Source records
- pubmed · retrieved 2026-11-10T11:29:50.546Z