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 Electroencephalography (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.

流程工业依靠操作人员的有效决策来确保安全。控制室操作员从DCS获取各种输入，对其进行解释，做出预后并通过适当的控制措施做出响应。为了有效地执行这些操作，操作员需要具有适当的过程心理模型。不良的心理模型会增加操作员的认知工作量，并使他们易于出错。传统上，操作员培训系统用于帮助操作员学习适当的心理模型。但是，训练期间使用的绩效评估指标并未明确说明他们在执行任务时的认知工作量。在这项工作中，我们证明这导致对操作员能力的错误评估。我们提出了一种基于脑电图（EEG）功率谱密度的指标，该指标可以量化认知工作量，并在培训过程中提供对操作员心理模型演变的详细了解。为了证明其实用性，我们进行了培训实验，十名参与者执行了438个任务。统计研究表明，提出的度量标准可以量化认知工作量，因此可用于准确评估操作员培训。