Robustness of a feedback optimization scheme with application to bioprocess manufacturing

TL;DR

This paper analyzes the robustness of a feedback optimization scheme in bioprocess manufacturing, extending existing convergence results to uncertain sensitivities, and demonstrates its potential through a biological application and synthetic example.

Contribution

It extends convergence analysis of feedback optimization to uncertain sensitivities and applies it to bioprocess manufacturing with a synthetic example.

Findings

Extended convergence results to uncertain sensitivities.

Demonstrated scheme's potential in bioprocess optimization.

Provided numerical data for reproducibility.

Abstract

In this work the robustness of a feedback optimization scheme is discussed. Previously known results in literature, on the convergence to local optima of the optimization problem of interest, are extended to the case where the sensitivities of the steady-state input-output map of the plant present bounded uncertainties. The application of the scheme to a biological setting, with the goal of maximizing the concentration of products of interest in a bioreactor, under a continuous perfusion framework, is suggested and the potential of the approach is exposed by means of a simple synthetic example. This extended version contains also the Numerical data for example in "Robustness of a feedback optimization scheme with application to bioprocess manufacturing" report, for the sake of reproducibility of the results.