Global optimization of symbolic surrogate process models based on Bayesian learning

Tim Forster, Daniel Vázquez, Gonzalo Guillé-Gosálbez

Producción científica: Capítulo del libroCapítulorevisión exhaustiva


In this work, we address the global optimization of process surrogates using Bayesian symbolic regression and deterministic global optimization algorithms. In contrast to other surrogates of process models that are hard to (globally) optimize, e.g., artificial neural networks or Gaussian processes, symbolic regression leads to a closed-form mathematical expression describing the observed data that can subsequently be globally optimized using off-the-shelf deterministic solvers. After providing an introductory example, we show the capabilities of our approach in the optimization of a methanol production plant. We further discuss the model accuracy, CPU times for model building and optimization, and outline the advantages and limitations of the proposed strategy.

Idioma originalInglés
Título de la publicación alojadaComputer Aided Chemical Engineering
EditorialElsevier B.V.
Número de páginas6
EstadoPublicada - ene 2023
Publicado de forma externa

Serie de la publicación

NombreComputer Aided Chemical Engineering
ISSN (versión impresa)1570-7946


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