  In complex industrial and chemical process control rooms, effective
decision-making is crucial for safety and efficiency. 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
influence 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
effectiveness 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.
