Journal
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
Volume 150, Issue -, Pages 51-67Publisher
ELSEVIER
DOI: 10.1016/j.psep.2021.03.050
Keywords
Training; Operator performance; Mental model; Cognitive workload; Clustering
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Process industries rely on effective decision-making by human operators to ensure safety. Control room operators need appropriate mental models of the process to perform effectively. Training systems should consider operators' cognitive workload to assess their abilities accurately.
Process industries rely on effective decision-making by human operators to ensure safety. Control room operators acquire various inputs from the DCS, interpret them, make a prognosis, and respond through appropriate control actions. In order to perform these effectively, the operator needs to have appropriate mental models of the process. Poor mental models would increase the operator's cognitive workload and make them prone to errors. Traditionally, operator training systems are used to help operators learn appropriate mental models. However, performance assessment metrics used during training do not explicitly account for their cognitive workload while performing a task. In this work, we demonstrate that this leads to an incorrect assessment of operators' abilities. We propose an Electroencephalogra-phy (EEG) power spectral density-based metric that can quantify the cognitive workload and provide detailed insight into the evolution of the operator's mental models during training. To demonstrate its utility, we have conducted training experiments with ten participants performing 438 tasks. Statistical studies reveal that the proposed metric can quantify the cognitive workload and therefore be used to assess operator training accurately. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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