In-Situ Model Validation for Continuous Processes Using In-Network Computing

TL;DR

This paper introduces CIVIC, an in-network computing system for real-time validation of industrial process models, enabling quick detection of process deviations to prevent damage and improve control reliability.

Contribution

It presents a novel in-network computing approach for continuous, in-situ validation of control models in industrial processes, enhancing responsiveness and scalability.

Findings

High detection accuracy achieved in lab-scale water treatment

Feasibility demonstrated on Intel Tofino-based switch

Supports timely process reconfiguration or shutdown

Abstract

The advancing industrial digitalization enables evolved process control schemes that rely on accurate models learned through data-driven approaches. While they provide high control performance and are robust to smaller deviations, a larger change in process behavior can pose significant challenges, in the worst case even leading to a damaged process plant. Hence, it is important to frequently assess the fit between the model and the actual process behavior. As the number of controlled processes and associated data volumes increase, the need for lightweight and fast reacting assessment solutions also increases. In this paper, we propose CIVIC, an in-network computing-based solution for Continuous In-situ Validation of Industrial Control models. In short, CIVIC monitors relevant process variables and detects different process states through comparison with a priori knowledge about the desired process behavior. This detection can then be leveraged to, e.g., shut down the process or trigger a reconfiguration. We prototype CIVIC on an Intel Tofino-based switch and apply it to a lab-scale water treatment plant. Our results show that we can achieve a high detection accuracy, proving that such monitoring systems are feasible and sensible.