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Optimization of a biotechnological multiproduct batch plant design for the manufacture of four different products: A real case scenario
Author(s) -
Sandoval Gabriela,
Espinoza Daniel,
Figueroa Nicolas,
Asenjo Juan A.
Publication year - 2017
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.26260
Subject(s) - process engineering , integer programming , computer science , production (economics) , batch processing , product (mathematics) , biochemical engineering , mathematics , engineering , algorithm , geometry , economics , macroeconomics , programming language
In this work a biotechnological multiproduct batch plant that manufactures four different recombinant proteins for human application is described in some detail. This batch plant design is then optimized with regards to the size of equipment using a mixed‐integer linear programming (MILP) formulation recently developed by us in order to find a hypothetical new biotechnological batch plant based on the information of real known processes for the production of the four recombinant protein products. The real plant was divided for practical purposes into two sub‐processes or facilities: a fermentation facility and a purification facility. Knowing the specific steps conforming the downstream processing of each product, size, and time factors were computed and used as parameters to solve the aforementioned MILP reformulation. New constraints were included to permit the selection of some equipment—such as centrifuges and membrane filters—in a discrete set of sizes. For equipment that can be built according to customer needs—such as reactors—the original formulation was retained. Computational results show the ability of this optimization methodology to deal with real data giving reliable solutions for a multi‐product batch plant composed of 44 unit operations in a relatively small amount of time showing that in the case studied it is possible to save up to a 66% of the capital investment in equipment given the cost data used. Biotechnol. Bioeng. 2017;114: 1252–1263. © 2017 Wiley Periodicals, Inc.