Computer-aided design of formulated products: A bridge design of experiments for ingredient selection
Liwei Cao, Danilo Russo, Emily Matthews, Alexei A. Lapkin, David C. Woods · Computers & Chemical Engineering · 2022
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.