A novel efficient optimisation system for purification process synthesis

Protein purification through chromatographic processes has been broadly used in the biopharmaceutical industry over the last decades, but still remains a major bottleneck. In this work, we address the challenge of selecting appropriate chromatographic steps, along with product collecting timeline for separating the target protein from the contaminants in a multicomponent mixture. A novel mixed integer linear programming (MILP) model for purification process synthesis is proposed. The model allows product losses and is tested on three example protein mixtures, containing up to 13 contaminants and selecting from a set of up to 21 candidate steps. The results are compared with previous literature models attempting to solve the same problem and show that the proposed approach offers significant gains in computational efficiency without compromising the quality of the solution.

► This paper presents an MILP modelling framework for the optimal purification process synthesis.
► The proposed models have been applied to three different example protein mixtures containing up to 13 contaminants and selecting from a set of up to 21 candidates.
► The results of the examples highlight the increase in computational efficiency.