  We discuss the application of a nonlinear model predictive control (MPC) and
a moving horizon estimation (MHE) to achieve an optimal operation of \textit{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 \textit{in silico} simulations for
bacterial fed-batch cultivation.
