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Robust optimization is a method for handling uncertainty by optimizing control inputs over a series of model realizations.
A novel on-line optimizing control strategy for wall-cooled fixed-bed reactors is presented.
Addressing this issue, we propose a new energy optimizing control system for the LNG process.
Optimizing control of E. coli cultures depends on the availability of appropriate on-line sensors for the main culture components.
Optimizing control of Escherichia coli cultures depends on the availability of appropriate on-line sensors for the main culture components.
Based on this characterization, fault accommodation strategies that guarantee closed-loop stability while simultaneously optimizing control and communication system resources are devised.
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In this paper, we apply the self-optimizing control ideas to find optimal controlled variables in the regulatory layer.
Controlled variables should be selected for their self-optimizing control performance and controllability while ensuring satisfactory performance in terms of dead-time and closed loop interactions.
This paper presents a novel data-driven approach for selecting controlled variables (CVs) following the self-optimizing control (SOC) strategy.
Furthermore, we present a method for finding optimal combinations of measurements for local self-optimizing control, for both of worst- and average-case loss minimization.
Self-optimizing control is a promising method for selection of controlled variables (CVs) from available measurements.
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CEO of Professional Science Editing for Scientists @ prosciediting.com