A machine learning approach for the surrogate modeling of uncertain distributed process engineering models

© 2022 Institution of Chemical EngineersTo boost process operation modern chemical technology can require detailed mathematical descriptions of such complex, interacting and nonlinear systems, specially when experiments or pilot plant data are lacking. In some cases, these process models are formulated in terms of partial differential equations, which, in turn are hard to solve due the high demand of computational resources. However, the recent access to large data sets has allowed to address the simulation of these complex chemical systems by machine learning schemes. This new approach has notable strengths over traditional methods, such as flexibility, relative easy implementation and fastest performance. The proposal is to build surrogate models to approximate system behavior by making use of massive data information. Nonetheless, one of the principal drawbacks of these methods is the lack of understanding and the inherent uncertainty related to them. This paper explores the capability of different machine learning techniques for modeling different chemical process with different non-linear behavior. Furthermore, to handle the uncertainty in the models and interpret the confidentiality of the results, a probabilistic gaussian machine learning framework was leveraged.

A machine learning approach for the surrogate modeling of uncertain distributed process engineering models Academic Article in Scopus