Model predictive control guided with optimal experimental design for pulse-based parallel cultivation

TL;DR

This paper presents a hierarchical control framework combining model predictive control with optimal experimental design to enhance parameter estimation in pulse-based parallel cultivations, addressing complexity and constraints.

Contribution

It introduces an auxiliary lower-level controller to track high-level optimal feeding strategies in real-time for parallel cultivation systems.

Findings

Effective online control of feeding strategies demonstrated in simulations.

Improved parameter estimation accuracy in parallel cultivations.

Framework handles operational constraints efficiently.

Abstract

Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model. With the recent developments in miniaturization and parallelization of cultivation platforms for high-throughput screening of optimal growth conditions massive amounts of informative data can be generated with few experiments. Increasing the quantity of the data means to increase the number of parameters and experimental design variables which might deteriorate the identifiability and hamper the online computation of optimal inputs. To reduce the problem complexity, in this work, we introduce an auxiliary controller at a lower level that tracks the optimal feeding strategy computed by a high-level optimizer in an online fashion. The hierarchical framework is especially interesting for the operation under constraints. The key aspect of this method are discussed together with an in silico study considering parallel glucose limited bacterial fed batch cultivations.