2022/03/14 by Jong Woo Kim, Kim, Jong Woo, Niels Krausch +11 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · #Advanced Control Systems Optimization #FOS: Biological sciences #FOS: Electrical engineering #Microbial Metabolic Engineering and Bioproduction #Quantitative Methods (q-bio.QM) #Systems and Control (eess.SY) #Viral Infectious Diseases and Gene Expression in Insects #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2203.07211
openalex publication_date 2022/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of E. coli fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput microbioreactor platform at a 10 mL scale. The robotic island in question can run up to 48 fed-batch processes in parallel with automated liquid handling and online and at-line analytics. The implementation of the model-based monitoring and control framework reveals that there are mainly three challenges that need to be addressed; First, the inputs are given in an instantaneous pulsed form by bolus injections, second, online and at-line measurement frequencies are severely imbalanced, and third, optimization for the distinctive multiple reactors can be either parallelized or integrated. We address these challenges by incorporating the concept of impulsive control systems, formulating multi-rate MHE with identifiability analysis, and suggesting criteria for deciding the reactor configuration. In this study, we present the key elements and background theory of the implementation with in silico simulations for bacterial fed-batch cultivation.