Optimization of a biotechnological multiproduct batch plant design for the manufacture of four different products: A real case scenario

Abstract

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.

Synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions

“…1a. These three stages have been formulated after careful consideration of previous literature and reports on bioseparations and downstream processing by Belter, Cussler and Hu [31], Harrison [32,80], Chisti [81][82][83][84][85], Asenjo [16,22,27,86,87], and Bogle [39,88,89]. Each stage has multiple technologies available for every task, as shown in Table 1.…”

“…In an evolutionary modification, designers make changes to known flowsheets for similar processes to meet new objectives and constraints. An optimization model can also be formulated to facilitate the comparison between different flowsheets and determination of process variables such as flowrates, operating temperatures, and pressures [22][23][24][25][26][27]. However, these two methods are not feasible for cases where a large number of different technologies is available for comparison.…”

Parallel Hybrid Metaheuristics Approach for Optimal Selection of Production Equipment

“…The problem of optimal design of multi-product batch plants is typically formulated as a mixed integer nonlinear programming (MINLP) problem [2]. Many nonlinear models for batch design, which are based on the assumption of continuous sizes, can be reformulated as mixed integer linear programming (MILP) problems when sizes are restricted to discrete values [3]. Finding an optimal solution to this NP-hard problem can be a difficult task due to the so-called "combinatorial explosion" -the number of combinations to be examined grows exponentially, such that even the fastest computers require an intolerable amount of time to analyze them.…”