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

Abstract

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 10 mL scale. The robotic island in question can run up to 48 fed-batch processes in parallel with an automated liquid handling and online and atline analytics. The implementation of the model-based monitoring and control framework reveals that there are mainly three challenges which need to be addressed; First, the inputs are given in an instantaneous pulsed form by bolus injections, second, online and atline measurement frequencies are severely imbalanced, and third, optimization for the distinctive multiple reactors can

High‐throughput screening of optimal process conditions using model predictive control

“…The last term is the summed difference between the predicted outputs h(•) as a function of the states x t ( ) The MPC calculates optimal inputs to maximize biomass at the end of the feeding phase, considering that the DOT should not drop below a predefined threshold of 30%. A detailed description of the MPC and its mathematical formulation can be found in Kim et al, (2022). The general problem can be written as follows:…”

“…The model has 6 differential states, 1 control input, and 18 parameters in total. A complete overview about the equations of the macro kinetic growth model and the meaning of the respective parameters can be found inKim et al (2022).…”