Liu, S;
Papageorgiou, LG;
(2018)
Multi-objective optimisation for biopharmaceutical manufacturing under uncertainty.
Computers & Chemical Engineering
, 119
pp. 383-393.
10.1016/j.compchemeng.2018.09.015.
Preview |
Text
1-s2.0-S0098135418306914-main.pdf - Published Version Download (1MB) | Preview |
Abstract
This work addresses the multi-objective optimisation of manufacturing strategies of monoclonal antibodies under uncertainty. The chromatography sequencing and column sizing strategies, including resin at each chromatography step, number of columns, column diameters and bed heights, and number of cycles per batch, are optimised. The objective functions simultaneously minimise the cost of goods per gram and maximise the impurity reduction ability of the purification process. Three parameters are treated as uncertainties, including bioreactor titre, and chromatography yield and capability to remove impurities. Using chance constraint programming techniques, a multi-objective mixed integer optimisation model is proposed. Adapting both ε-constraint method and Dinkelbach's algorithm, an iterative solution approach is developed for Pareto-optimal solutions. The proposed model and approach are applied to an industrially-relevant example, demonstrating the benefits of the proposed model through Monte Carlo simulation. The sensitivity analysis of the confidence levels used in the chance constraints of the proposed model is also conducted.
| Type: | Article |
|---|---|
| Title: | Multi-objective optimisation for biopharmaceutical manufacturing under uncertainty |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.compchemeng.2018.09.015 |
| Publisher version: | https://doi.org/10.1016/j.compchemeng.2018.09.015 |
| Language: | English |
| Additional information: | © 2018 The Authors. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) |
| Keywords: | Biopharmaceutical manufacturing, Multi-objective optimisation, Uncertainty, Chance constrained programming, Mixed integer programming |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10063425 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
