ORCID

Abstract

Data from psychophysiological measures can offer new insight into control room operators’ behaviour, cognition, and mental workload status. This is particularly helpful when assessing capacity to respond to critical plant conditions such as alarm response scenarios. However, wearable tools such as eye tracking and electroencephalography caps can be perceived as intrusive and unsuitable for daily operations. Therefore, this article examines the potential of using real-time data from process and operator-system interactions during abnormal scenarios, recorded and retrieved from the distributed control system’s historian or process log, to provide insight into operator behaviour and predict their response outcomes without intruding on daily tasks. Data for this study were obtained from a design of experiment using a formaldehyde production plant simulator and four human-in-the-loop support configurations. A comparison between configurations in terms of both behaviour and performance is presented. Then, a step-wise logistic regression and a Bayesian network model were used to predict operator error. The results identified predictive metrics, discussed in terms of their value as precursors of overall system performance in alarm response scenarios. Knowledge of relevant and predictive behavioural metrics accessible in real time can better equip decision-makers to predict outcomes and provide timely support measures for operators.

Keywords

Alarm handling, Bayesian network, Control rooms, Decision support, Human factors, Logistic regression, Operational data

Publication Date

2026-01-01

Publication Title

Reliability Engineering and System Safety

Volume

272

ISSN

0951-8320

Deposit Date

2026-05-11

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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