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

We discuss the application of a nonlinear model predictive control (MPC) and moving horizon estimation (MHE) framework to achieve optimal operation of E. coli fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput mini-bioreactor platform at a 10 mL scale. The robotic facility can run up to 48 fed-batch processes in parallel with automated liquid handling, online and at-line analytics. Three main challenges emerge in implementing the model-based monitoring and control framework: 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 cultivations.