Downstream bioprocessing and especially chromatographic steps, commonly used for the purification of multicomponent systems, are significant cost drivers in the production of therapeutic proteins. There has been an increased interest in the development of systematic methods for the design of such processes, and the appropriate selection of a series of chromatographic steps is still a major challenge to be addressed. Several models have been developed previously but have assumed that 100% recovery of the desired product is obtained at each chromatographic step. In this work, a mathematical framework is proposed, based on mixed integer optimisation techniques, that removes this assumption and allows full flexibility on the position of retention time cut-points, between which the desired product fraction is collected. The proposed model is demonstrated on three example protein mixtures, each containing up to 13 contaminants and selecting from a set of up to 21 candidate steps. The proposed model results in a reduction of one to three chromatographic steps over solutions that no losses are allowed.

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Predicted water shortages assign water treatment a leading role in improving water resources management. One of the main challenges associated with the processes remains early stage design of techno-economically optimised purification. This work addresses the current gap by undertaking a whole-system approach of flowsheet synthesis for the production of water at desired purity at minimum overall cost. The optimisation problem was formulated as a mixed integer non-linear programming (MINLP) model. Two case studies were presented which incorporated the most common commercial technologies and the major pollution indicators, such as chemical oxygen demand (COD), dissolved organic carbon (DOC), total suspended solids (TSS) and total dissolved solids (TDS).The results were analysed and compared to existing guidelines in order to examine the applicability of the proposed approach.

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