Integrated Optimization of Upstream and Downstream Processing in Biopharmaceutical Manufacturing under Uncertainty: A Chance Constrained Programming Approach
Author(s) -
Songsong Liu,
Suzanne S. Farid,
Lazaros G. Papageorgiou
Publication year - 2016
Publication title -
industrial and engineering chemistry research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.878
H-Index - 221
eISSN - 1520-5045
pISSN - 0888-5885
DOI - 10.1021/acs.iecr.5b04403
Subject(s) - upstream (networking) , downstream processing , sizing , downstream (manufacturing) , integer programming , upstream and downstream (dna) , computer science , digital signal processing , mathematical optimization , process engineering , engineering , chemistry , chromatography , mathematics , algorithm , computer hardware , computer network , operations management , organic chemistry
This work addresses the integrated optimization of upstream and downstream processing strategies of a monoclonal antibody (mAb) under uncertainty. In the upstream processing (USP), the bioreactor sizing strategies are optimized, while in the downstream processing (DSP), the chromatography sequencing and column sizing strategies, including the resin at each chromatography step, the number of columns, the column diameter and bed height, and the number of cycles per batch, are determined. Meanwhile, the product’s purity requirement is considered. Under the uncertainties of both upstream titer and chromatography resin yields, a stochastic mixed integer linear programming (MILP) model is developed, using chance constrained programming (CCP) techniques, to minimize the total cost of goods (COG). The model is applied to an industrially relevant example and the impact of different USP:DSP ratios is studied. The computational results of the stochastic optimization model illustrate its advantage over the determinis...
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