In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and effi- ciency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influ- ence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye- tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effec- tiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide valuable insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time. These predictions enable the development of more effective intervention strategies.

翻译：在复杂的工业和化工过程控制室中，有效的决策对安全与效率至关重要。本文实验评估了集成到改进型人机界面中基于AI的决策支持系统的影响与应用，该系统采用动态影响图、隐马尔可夫模型和深度强化学习。增强型支持系统旨在降低操作员工作负荷、提升态势感知能力，并根据系统和人员绩效的当前状态为操作员提供不同的干预策略。此类系统在信息过载场景（例如大量报警和输入在同一时间窗口内呈现）或新操作员培训中尤为有用。我们开展了涵盖47名参与者的跨数据综合分析，数据来源包括智能手表指标、眼动追踪数据、过程日志及问卷反馈等。结果表明，该方法在辅助决策、降低感知工作负荷及提升所考虑场景下的态势感知方面具有显著效果。此外，研究结果为了解参与者使用该系统时信息收集方式的差异提供了宝贵见解。这些发现在通过工艺与人机交互日志实时预测个体参与者的整体绩效及其成功处理装置故障及关联报警的能力方面尤为关键，从而能够制定更有效的干预策略。