Proactive monitoring in process control using predictive trend displays

Abstract

Process control such as that in the petrochemical industry is inherently difficult for humans to operate and monitor. Console operators need to manage hundreds of interrelated components using sluggish controls in a high-risk environment. They need to keep the process stable while optimizing production, which puts variables near plant operating limits. Any anomaly or upset has to be resolved quickly before the severity of the problem escalates. All these tasks are performed using a control console called the Distributed Control System (DCS). This project was initiated with the goal of exploring viable information visualizations on DCS displays to support proactive monitoring in console operators. While operators may choose to be alerted of and react to problems through the alarms on the DCS, expert operators prefer to stay proactive, and seize the problem before it disrupts the stability of the process. Being proactive requires prediction, a mental process which is not well understood and difficult to perform accurately. A series of literature reviews was conducted to find out more about the concepts related to the psychology of prediction, followed by various engineering elements, particularly in process control, that aid prediction. Currently there are no explicit predictive displays for process control. Four studies were conducted during the span of this project, each filling in knowledge gaps either not found in current literature, or provided empirical proof-of-concept for a viable predictive tool that improved control performance. The first qualitative investigation revealed how expert console operators derive, update and apply their mental models while at work. A second qualitative investigation documented the use of trend information displayed on current DCS consoles with the purpose of facilitating proactive monitoring. A simulator study was conducted which found operator performance benefits from using a trend-based predictive display with multi-variate rate-of-change cues. A second, final experiment featured a high-fidelity schematic display and a single-variate rate-of-change algorithm. Final results showed a viable prototype predictive visualization and algorithm for further industry application.