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Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO(2) Capture Technologies
[Image: see text] Process modeling has become a fundamental tool to guide experimental work. Unfortunately, process models based on first principles can be expensive to develop and evaluate, and hard to use, particularly when convergence issues arise. This work proves that Bayesian symbolic learning...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670717/ https://www.ncbi.nlm.nih.gov/pubmed/36406548 http://dx.doi.org/10.1021/acsomega.2c04736 |