A multi-level meta-heuristic algorithm for the optimisation of antibody purification processes

A meta-heuristic optimisation approach using genetic algorithms is presented to aid the design of multi-product biopharmaceutical facilities. Different levels of decision are addressed by the tool and multiple process and business criteria are used to evaluate each alternative. At the purification sequence level, feasible 2-step and 3-step chromatography sequences were generated and evaluated in terms of cost of goods, time and purity metrics. At the unit operation level, a genetic algorithm was developed to determine the best equipment sizing strategy, in terms of column dimensions and number of cycles. The industrial case study provides novel insights that allow the identification of the most cost-effective purification sequences and equipment sizing strategies that meet demand and purity targets for each product in the facility. Graphical plots to visualise trade-offs in the set of optimal solutions are presented so as to enhance the decision making process. For example, the impact of different impurity profiles on the feasibility of the optimal sequences is highlighted using advanced discrete contour plots whilst bubble plots are used to illustrate the impact of different user preferences on the set of optimal equipment sizing strategies.

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