HybridGamma: A thermodynamically consistent framework for hybrid modelling of activity coefficients
Year: 2023
Type: article
Abstract: Predicting molecular interactions is a crucial step for chemical process modelling.It requires the full knowledge of the analyzed system, however, this is often impossible in complex real-world cases.Machine learning (ML) techniques overcome this bottleneck and enhance systems predictability using data.Hybrid modelling (HM) is an established technique combining first-principle information and ML techniques.This work introduces a mathematical framework to predict activity coefficients employing HM approach.The obtained models are physically consistent and can handle systems with unknown components or external sources of deviation.The framework is validated on experimental and in-silico cases employing different training approaches.In all the tested cases, the HM showed remarkable prediction capabilities with coefficients of determination R 2 above 0.98 for the predicted variables.This work proposes and develops a novel way to approach the HM of molecular interactions by embedding physical laws within the model structure.We encountered three main benefits in applying thermodynamically consistent HMs for activity coefficients: the reduction of tenable parameters, the increased prediction capabilities, and the physicalconsistent behavior of the model.
Source: Chemical Engineering Journal
Institutions: KU Leuven, University of Antwerp, University of Vienna
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Award: G7294391378
OpenAlex ID: W4386893333