The application of a Pareto optimisation method in the design of an integrated bioprocess

Abstract

A rapid method for designing integrated bioprocesses, using a combination of a windows of operation and a Pareto optimisation approach, is described in this paper. Within bioprocesses, multiple objectives are common, and achieving a satisfactory trade-off amongst the design objectives is crucial. Conventional optimisation results in the identification of the best operating policy for a given desired performance but gives little insight into how the process performance changes in the vicinity of the solution. In this paper, we explore the use of a Pareto optimisation technique to locate the optimal conditions for an integrated bioprocessing sequence and the benefits of first reducing the feasible space by the development of a series of windows of operation to provide a smaller search area for the optimisation. The final results are then presented in performance trade-off graphs and look-up tables, which give the design engineer an easily manageable solution set to work with. In this way, the decision-making procedure for design is made faster and more transparent. Two case studies illustrate the results from this integrated design methodology, some of which are counter-intuitive compared with the general design experience.

Design strategies for integrated protein purification processes: challenges, progress and outlook

“…50,53 TitchenerHooker and coworkers 54 -56 have proposed optimization approaches based on a graphical representation of the feasible windows of operation for integrated protein recovery bioprocesses, and the pareto method for locating the optimal operating points within the feasible regions. 57,58 Asenjo and coworkers 59 have attempted the use of a MINLP formulation for developing strategies for the simultaneous optimization of the structure and process variables of an integrated protein production plant. Bogle and co-workers 23,60 presented a synthesis methodology for integrated biochemical processes in which downstream processing unit operations were first screened using physicochemical properties information, followed by a systematic evaluation of the generated superstructure using an implicit enumeration synthesis tool in which the MINLP problem is converted into a graph generation and search problem.…”

Modelling and Fuzzy-Decision-Making of Batch Cultivation of Saccharomyces cerevisiae using Different Mixing Systems

“…In order to define concisely the Pareto optimal solution, we have introduced the following definition 8,10 Definition 1. The feasible region in input space, W, is the set of all admissible control variables and the system parameters that satisfy the system constraints…”

“…This theorem is used to guarantee that the unique optimal solution of (11) is a Pareto solution to the multiple objective optimal control problems (7)- (8). The statement of this theorem does not guarantee a unique optimal solution to (11).…”

“…Theorem 2 is used to guarantee that the unique optimal solution to the maximizing decision problem (11) is an M-Pareto optimal solution of fuzzy problems (7)- (8). The key point for using this theorem is to find a unique optimal solution to problem (11).…”

“…The competition between multiple objective functions is a key distinction between multiple objective optimisation and traditional single objective optimisation. [5][6][7] A Pareto optimisation technique has been used in study 8 to locate the optimal conditions for an integrated bioprocessing sequence. The benefits of first reducing the feasible space by development provide a smaller search area for the optimisation.…”