Machine learning and hybrid modelling for reaction engineering : theory and applications / 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...
Saved in:
| Online Access: |
Full Text (via EBSCO) |
|---|---|
| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Cambridge :
Royal Society of Chemistry,
[2024]
|
| Series: | Theoretical and computational chemistry series ;
no. 26 |
| Subjects: |
Table of Contents:
- 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.