Scale-up of complex molecular reaction system by hybrid mechanistic modeling and deep transfer learning Article Swipe
Zhengyu Chen
,
Xie Yong-qing
,
Chunming Xu
,
Linzhou Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-63982-2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-63982-2
The scale-up of chemical processes involves substantial changes in reactor size, operational modes, and data characteristics, leading to significant challenges in predicting product distribution across scales. This study presents a unified modeling framework that integrates the mechanistic model with deep transfer learning to accelerate chemical process scale-up. The framework is demonstrated through a case study on naphtha fluid catalytic cracking. A molecular-level kinetic model was developed from laboratory-scale experimental data, and a deep neural network was designed and trained to represent complex molecular reaction systems. To address the challenge of discrepancies in data types at various scales, a property-informed transfer learning strategy was developed by incorporating bulk property equations into the neural network. This approach enabled automated prediction of pilot-scale product distribution with minimal data. Moreover, process conditions of the pilot plant were optimized using a multi-objective optimization algorithm.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-025-63982-2
- https://www.nature.com/articles/s41467-025-63982-2.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 45
- OpenAlex ID
- https://openalex.org/W4414896200
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https://openalex.org/W4414896200Canonical identifier for this work in OpenAlex
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Scale-up of complex molecular reaction system by hybrid mechanistic modeling and deep transfer learningWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-07Full publication date if available
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-
Zhengyu Chen, Xie Yong-qing, Chunming Xu, Linzhou ZhangList of authors in order
- Landing page
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https://doi.org/10.1038/s41467-025-63982-2Publisher landing page
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https://www.nature.com/articles/s41467-025-63982-2.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41467-025-63982-2.pdfDirect OA link when available
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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45Number of works referenced by this work
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| abstract_inverted_index.equations | 108 |
| abstract_inverted_index.framework | 32, 48 |
| abstract_inverted_index.molecular | 82 |
| abstract_inverted_index.optimized | 133 |
| abstract_inverted_index.processes | 4 |
| abstract_inverted_index.represent | 80 |
| abstract_inverted_index.scale-up. | 46 |
| abstract_inverted_index.accelerate | 43 |
| abstract_inverted_index.algorithm. | 138 |
| abstract_inverted_index.challenges | 19 |
| abstract_inverted_index.conditions | 127 |
| abstract_inverted_index.integrates | 34 |
| abstract_inverted_index.predicting | 21 |
| abstract_inverted_index.prediction | 117 |
| abstract_inverted_index.mechanistic | 36 |
| abstract_inverted_index.operational | 11 |
| abstract_inverted_index.pilot-scale | 119 |
| abstract_inverted_index.significant | 18 |
| abstract_inverted_index.substantial | 6 |
| abstract_inverted_index.demonstrated | 50 |
| abstract_inverted_index.distribution | 23, 121 |
| abstract_inverted_index.experimental | 68 |
| abstract_inverted_index.optimization | 137 |
| abstract_inverted_index.discrepancies | 90 |
| abstract_inverted_index.incorporating | 105 |
| abstract_inverted_index.molecular-level | 61 |
| abstract_inverted_index.multi-objective | 136 |
| abstract_inverted_index.characteristics, | 15 |
| abstract_inverted_index.laboratory-scale | 67 |
| abstract_inverted_index.property-informed | 98 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.9070117 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |