On-Line Reoptimization of Mammalian Fed-Batch Culture Using a Nonlinear Model Predictive Controller

Tomoki Ohkubo, Yuichi Sakumura, Katsuyuki Kunida

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalNew Generation Computing
DOIs
Publication statusAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications

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