The DOI matches its Crossref record.
Neural Networks in Bioprocessing and Chemical Engineering
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Cited by
Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Zendehboudi, S., Rezaei, N., & Lohi, A. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.06.051 · Cited by 394
Applications of artificial neural networks in chemical engineering
Himmelblau, D. M. (2000) · Korean Journal of Chemical Engineering · doi:10.1007/bf02706848 · Cited by 273
A review on machine learning approaches for microalgae cultivation systems
Syed, T., Krujatz, F., Ihadjadene, Y., Mühlstädt, G., Hamedi, H., Mädler, J., & Urbas, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108248 · Cited by 92
Jung, S., & Lee, S. B. (2006) · Biotechnology Progress · doi:10.1021/bp0600886 · Cited by 26
Metadata
- Venue
- Elsevier eBooks
- Publisher
- Elsevier
- Published
- 1995
- OpenAlex
- W2139452320
- Cited by
- 351
- References
- 0
- FWCI
- 1.76
- Type
- book
- Language
- en
- Open access
- green
About these checks
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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- CoChat. (2026). Citation check: Neural Networks in Bioprocessing and Chemical Engineering. https://app.cochat.ai/papers/10.1016/c2009-0-21189-5
- The paper
- Neural Networks in Bioprocessing and Chemical Engineering. (1995). Elsevier eBooks. https://doi.org/10.1016/c2009-0-21189-5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
