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Closed‐loop optimization of chromatography column sizing strategies in biopharmaceutical manufacture
Author(s) -
Allmendinger Richard,
Simaria Ana S.,
Turner Richard,
Farid Suzanne S.
Publication year - 2014
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.4267
Subject(s) - sizing , identification (biology) , computer science , process (computing) , set (abstract data type) , optimization problem , evolutionary algorithm , multi objective optimization , mathematical optimization , engineering , machine learning , mathematics , algorithm , chemistry , botany , organic chemistry , biology , programming language , operating system
BACKGROUND This paper considers a real‐world optimization problem involving the identification of cost‐effective equipment sizing strategies for the sequence of chromatography steps employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters, and time‐consuming fitness evaluations. RESULTS An industrially‐relevant case study is used to illustrate that evolutionary algorithms can identify chromatography sizing strategies with significant improvements in performance criteria related to process cost, time and product waste over the base case. The results demonstrate also 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. Finally, a data‐visualization tool is employed to illustrate how user preferences can be accounted for when it comes to selecting a sizing strategy to be implemented in a real industrial setting. CONCLUSION This work demonstrates that closed‐loop evolutionary optimization, when tuned properly and combined with a detailed manufacturing cost model, acts as a powerful decisional tool for the identification of cost‐effective purification strategies. © 2013 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.