Computer-aided design of formulated products: A bridge design of experiments for ingredient selection. (January 2023)

• Record Type: Journal Article
• Authors: Cao, Liwei; Russo, Danilo; Matthews, Emily; Lapkin, Alexei; Woods, David
• Is Part Of: Computers & chemical engineering. Volume 169(2023)
• Publication Date: 2023-01
• Subjects: Design of Experiments (DoE) -- Bayesian optimization -- Product design -- Gaussian processes -- Machine Learning (ML); Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2022.108083
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A bridge-design of experiments was developed for formulations optimization. The methodology allowed to simultaneously optimize discrete and continuous variables. The methodology allowed to choose a subset of ingredients from n available ones. Formulations were optimized with an automated platform in 17 working days. Abstract: Formulations are ubiquitous in many industries. As formulations are being modified and re-developed to include more renewable and recyclable ingredients, the speed of formulations development becomes important. This study expands on the previous work demonstrating successful application of multi-objective Bayesian optimization to design of formulations within a restricted set of the available ingredients. Here we develop an approach that resolves the un-solved to date problem in algorithmic formulations development, when a subset of ingredients should be chosen from a larger available pool of suitable ingredients. The new DoE algorithm was demonstrated in a workflow making use of a 'make and test' formulation robots. The developed new DoE procedure demonstrated an efficient selection of a subset of ingredients from a larger number of the available ones, optimizing their concentration and allowing assignment of differential priorities to the optimization objectives.