ARRTOC: Adversarially Robust Real-Time Optimization and Control
Abstract
Real-Time Optimization (RTO) plays a crucial role in the process operation hierarchy by determining optimal set-points for the lower-level controllers. However, at the control layer, these set-points may be difficult to track due to challenges in implementation as a result of disturbances, measurement noise, and actuator performance limitations. To address this, in this paper, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC addresses this issue by finding set-points which are both optimal and inherently robust to implementation errors at the control layers. ARRTOC draws inspiration from adversarial machine learning, offering a novel constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. By integrating controller design with RTO, ARRTOC enhances overall system performance and robustness by ensuring the chosen set-points are tailored to the underlying controller designs. To validate our claims, we present three case studies: an illustrative example, a bioreactor case study, and a multi-loop evaporator process. The proposed approach is found to improve RTO objectives, such as profit, by as much as in some case studies compared to RTO formulations which ignore the performance of the control layers.
Cite
@article{arxiv.2309.04386,
title = {ARRTOC: Adversarially Robust Real-Time Optimization and Control},
author = {Akhil Ahmed and Ehecatl Antonio del Rio-Chanona and Mehmet Mercangoz},
journal= {arXiv preprint arXiv:2309.04386},
year = {2024}
}