Efficient Discovery of Chromatography Equipment Sizing Strategies for Antibody Purification Processes Using Evolutionary Computing

Abstract

This paper considers a real-world optimization problem involving the discovery of cost-effective equipment sizing strategies for the chromatography technique employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters (and thus noise), and time-consuming fitness evaluations. After introducing this problem, an industrially-relevant case study is used to demonstrate that evolutionary algorithms perform best when infeasible solutions are repaired intelligently, the population size is set appropriately, and elitism is combined with a low number of Monte Carlo trials (needed to account for uncertainty). Adopting this setup turns out to be more important for scenarios where less time is available for the purification process.