Classifier surrogates to ensure phase stability in optimisation-based design of solvent mixtures
File(s)
Published version
Author(s)
Karia, Tanuj
Chaparro, Gustavo
Chachuat, Benoit
Adjiman, Claire S
Type
Journal Article
Abstract
The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAMbD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAMbD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the in silico design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.
Date Issued
2025-03-01
Date Acceptance
2024-11-25
Citation
Digital Chemical Engineering, 2025, 14
ISSN
2772-5081
Publisher
Elsevier
Journal / Book Title
Digital Chemical Engineering
Volume
14
Copyright Statement
© 2024 Published by Elsevier Ltd on behalf of Institution of Chemical Engineers (IChemE). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Subjects
AIDED MOLECULAR DESIGN
ALGORITHM
Artificial neural network
BLENDS
Classifier
Computer-aided mixture/blend design
CRYSTALLIZATION
Engineering
Engineering, Chemical
FORMULATION
GLOBAL OPTIMIZATION
MINIMIZATION
Mixed-integer nonlinear programming
MULTICOMPONENT
MULTIPHASE EQUILIBRIUM CALCULATIONS
PARAMETER-ESTIMATION
Phase stability
Science & Technology
Surrogate-based optimisation
Technology
Publication Status
Published
Article Number
100200
Date Publish Online
2024-12-20