Machine learning and hybrid modelling for reaction engineering : theory and applications [E-Book] / edited by Dongda Zhang and Ehecatl Antonio del Río Chanona.
Over the last decade, there has been a significant shift from traditional mechanistic and empirical modelling into statistical and data-driven modelling for applications in reaction engineering. In particular, the integration of machine learning and first-principle models has demonstrated significan...
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Full text |
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Personal Name(s): | Zhang, Dongda, editor |
Rio Chanona, E. Antonio del, editor | |
Imprint: |
Cambridge :
Royal Society of Chemistry,
2024
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Physical Description: |
1 online resource (420 pages) |
Note: |
englisch |
ISBN: |
9781837670178 9781837670185 |
Series Title: |
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Theoretical and computational chemistry series ;
26 |
Subject (LOC): |
- Physical model construction
- Data-driven model construction
- Hybrid model construction
- Model structure identification
- Model uncertainty analysis
- Interpretable machine learning for kinetic rate model discovery
- Graph neural networks for the prediction of molecular structure-property relationships
- Reaction network simulation and model reduction
- Hybrid modeling under uncertainty: effects of model greyness, data quality, and data quantity
- A data-efficient transfer learning approach for new reaction system predictive modeling
- Constructing time-varying and history-dependent kinetic models via reinforcement learning
- Surrogate and multiscale modeling for (bio)reactor scale-up and visualization
- Statistical design of experiments for reaction modeling and optimization
- Autonomous synthesis and self-optimizing reactors
- Industrial data science for batch reactor monitoring and fault detection.