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We discuss the application of a nonlinear model predictive control (MPC) anda moving horizon estimation (MHE) to achieve an optimal operation of \textit{E.coli} fed-batch cultivations with intermittent bolus feeding. 24 parallelexperiments were considered in a high-throughput microbioreactor platform at a10 mL scale. The robotic island in question can run up to 48 fed-batchprocesses in parallel with automated liquid handling and online and at-lineanalytics. The implementation of the model-based monitoring and controlframework reveals that there are mainly three challenges that need to beaddressed; First, the inputs are given in an instantaneous pulsed form by bolusinjections, second, online and at-line measurement frequencies are severelyimbalanced, and third, optimization for the distinctive multiple reactors canbe either parallelized or integrated. We address these challenges byincorporating the concept of impulsive control systems, formulating multi-rateMHE with identifiability analysis, and suggesting criteria for deciding thereactor configuration. In this study, we present the key elements andbackground theory of the implementation with \textit{in silico} simulations forbacterial fed-batch cultivation.

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}