ARRTOC: Adversarially Robust Real-Time Optimization and Control

TL;DR

ARRTOC introduces an adversarially robust optimization method for real-time control systems, improving set-point robustness against disturbances and noise, and enhancing overall process performance and profit in various case studies.

Contribution

The paper presents ARRTOC, a novel adversarially robust optimization framework that integrates control layer considerations into RTO for improved robustness and performance.

Findings

ARRTOC improves RTO profit by up to 50%.

The approach enhances robustness against disturbances and noise.

Validated through three diverse case studies.

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 $50\%$ in some case studies compared to RTO formulations which ignore the performance of the control layers.