Work

ARRTOC: Adversarially Robust Real-Time Optimization and Control

Year: 2024

Type: article

Abstract: Real-Time Optimization (RTO) plays a crucial role in process operation by determining optimal set-points for lower-level controllers. However, tracking these set-points can be challenging at the control layer due to disturbances, measurement noise, and actuator limitations, leading to a mismatch between expected and achieved RTO benefits. To address this, 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. We present several case studies to validate our approach, including a bioreactor, a multi-loop evaporator process, and scenarios involving plant-model mismatch. These studies demonstrate that ARRTOC can improve realized RTO benefits by as much as 50% compared to traditional RTO formulations that do not account for control layer performance. • We introduce ARRTOC, an algorithm for selecting optimal, inherently robust set-points. • ARRTOC enhances system performance by aligning set-points with controller designs. • ARRTOC improves RTO objectives by as much as 50% in some case studies.

Source: Computers & Chemical Engineering

DOI: 10.1016/j.compchemeng.2024.108930

Authors: Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

Institution: Imperial College London

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,230