Optimization of a biotechnological multiproduct batch plant design for the manufacture of four different products: a real case scenario
Author
dc.contributor.author
Sandoval, Gabriela
Author
dc.contributor.author
Espinoza González, Daniel
Author
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Figueroa, Nicolas
Author
dc.contributor.author
Asenjo de Leuze, Juan
Admission date
dc.date.accessioned
2019-05-29T13:29:55Z
Available date
dc.date.available
2019-05-29T13:29:55Z
Publication date
dc.date.issued
2017
Cita de ítem
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Biotechnology and Bioengineering, Vol. 114, No. 6, June, 2017
Identifier
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10970290
Identifier
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00063592
Identifier
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10.1002/bit.26260
Identifier
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https://repositorio.uchile.cl/handle/2250/168880
Abstract
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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 multiproduct
batchplant 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.