Balaji Bharatwaajan, Shahab Mohammed Aatif, Srinivasan Babji, Srinivasan Rajagopalan
Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.
Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
Front Hum Neurosci. 2023 Feb 8;17:1038060. doi: 10.3389/fnhum.2023.1038060. eCollection 2023.
To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators' performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators' cognitive behavior. In addition, these cannot be used to predict operators' expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator's behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT's ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT's cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator's mental models. Additionally, the HDT can also enhance operators' decision-making during real-time operation.
为确保安全高效运行，流程工业中的操作人员必须根据时变信息及时做出决策。因此，对操作人员的绩效进行全面评估具有挑战性。当前的操作人员绩效评估方法主观且忽视了操作人员的认知行为。此外，这些方法无法用于预测操作人员在工厂运行过程中可能出现的新情况下的预期反应。本研究旨在开发一种人类数字孪生体（HDT），即使在各种异常情况下也能模拟控制室操作人员的行为。HDT是使用ACT-R（思维自适应控制-理性）认知架构开发的。它模仿人类操作员在监控过程并在异常情况下进行干预时的行为。我们进行了426次试验来测试HDT处理抗干扰任务的能力。在这些模拟中，我们改变了奖励和惩罚参数以向HDT提供反馈。我们使用10名人类受试者的眼动行为对HDT进行了验证，这些受试者完成了110项与HDT类似的抗干扰任务。结果表明，即使在处理异常情况时，HDT也表现出与人类受试者相似的注视行为。这些表明HDT的认知能力与人类操作员相当。作为可能的应用，所提出的HDT可用于生成异常情况下人类行为的大型数据库，然后可用于发现和纠正新手操作员心理模型中的缺陷。此外，HDT还可以在实时操作中增强操作员的决策能力。
