Abstract
Uncertainty is a common feature of biological systems, and model-free extremum-seeking control has proved a relevant approach to avoid the typical problems related to model-based optimization, e.g., time- and resource-consuming derivation and identification of dynamic models, and lack of robustness of optimal control. In this article, a review of the past and current trends in model-free extremum seeking is proposed with an emphasis on finding optimal operating conditions of bioprocesses. This review is illustrated with a simple simulation case study which allows a comparative evaluation of a few selected methods. Finally, some experimental case studies are discussed. As usual, practice lags behind theory, but recent developments confirm the applicability of the approach at the laboratory scale and are encouraging a transfer to industrial scale.
Highlights
Real-time optimization (RTO) of steady-state plants stems from computer-aided production engineering (CAPE) and aims at improving process performance through the optimization of a measurable criterion or objective function under economical, safety or quality constraints [1]
While this paper mostly focuses on the second category of methods, the first stream has brought a number of important results that are briefly reviewed
Lara-Cisneros et al [55] proposed the optimization of fed-batch bioreactors modeled by (3) using a sliding-mode extremum seeking control combined with a high-gain observer estimating the uncertain kinetics
Summary
Real-time optimization (RTO) of steady-state plants stems from computer-aided production engineering (CAPE) and aims at improving process performance through the optimization of a measurable criterion or objective function under economical, safety or quality constraints [1]. Direct input adaptation methods transforming the RTO into a feedback control problem constitute a third alternative exploiting the invariance properties of the steady-state plant. Necessary conditions of optimality (NCO) tracking, which uses the NCO as invariants to enforce optimality conditions [10,11,12] and gradient-based optimization They are considered the run-to-run optimization parent branches of both SOC and ESC; Processes 2020, 8, 1209; doi:10.3390/pr8101209 www.mdpi.com/journal/processes. Dynamic real-time optimization (DRTO) [1,13,14], which originates from the integration of RTO strategies, achieving process operating condition updates, in a model predictive control (MPC).
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