期刊
NEW GENERATION COMPUTING
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1007/s00354-023-00235-0
关键词
Bioprocess; Nonlinear model predictive control; Model-based optimization; Fed-batch culture; Process-model mismatch
This study investigates the feasibility and benefits of using NLMPC for online reoptimization in biopharmaceutical production, compensating for process-model mismatch. The results demonstrate that online reoptimization can improve the final product quality.
Fed-batch culture is widely used in biopharmaceutical production owing to its superior productivity; however, optimizing feeding trajectories remains a challenge. In this study, we investigated the feasibility and benefits of using a nonlinear model predictive controller (NLMPC) for on-line reoptimization in mammalian fed-batch culture to compensate for process-model mismatch (PMM). We simulated a monoclonal antibody production process using a standard kinetic model and deliberately introduced PMM via parameter errors. The NLMPC optimized feeding trajectories for a single-feed case, in which a mixture of glucose and glutamine is fed, and for a multiple-feed case, in which glucose and glutamine are fed independently. Our results demonstrate that on-line reoptimization successfully compensates for PMM, improving the final product mass compared to off-line optimization. This study highlights the potential of on-line reoptimization using NLMPCs in mammalian fed-batch culture, which can enhance product yield even in the presence of insufficient parameter estimation.
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