Decision support for process operators: Task loading in the days of big data

Abstract

Modern chemical processes rely on distributed control systems to make the repetitive and routine adjustments to maintain steady operation. Operators are still required to “supervise the (system) supervisor” and intervene when variables exceed pre-programmed parameters to avert major incidents. Research in human-computer interaction and advanced process control has often focused on data-driven methods for fault detection as distinct from operator effectiveness. In this paper, we explore the application of a novel data-driven fault-detection technique to enhance operator decision support. During a simulated abnormal event, three users attempted to diagnose the root cause of a process upset using a traditional or standard interface, then with the addition of causal maps, in a A-B-A single-subject design. The causal maps were derived using a hierarchical method that could be applied to a wide range of chemical processes as an online, adaptive augmentation for abnormal situation management. Using a think-aloud technique, the three participants developed high quality insights into the process without negatively impacting the overall task load. These preliminary findings challenge prevailing wisdom in process control interface design, which often focuses on de-cluttering displays at the cost of information resolution.