Author ORCID Identifier
1.1 MATHEMATICS, Applied mathematics, Probability, 2. ENGINEERING AND TECHNOLOGY, 2.3 MECHANICAL ENGINEERING, Chemical engineering (plants, products), Chemical process engineering
In today’s complex industrial environment, operators are often faced with challenging situations that require quick and accurate decision-making. The human-machine interface (HMI) can display too much information, leading to information overload and potentially compromising the operator’s ability to respond effectively. To address this challenge, decision support models are needed to assist operators in identifying and responding to potential safety incidents. In this paper, we present an experiment to evaluate the effectiveness of a recommendation system in addressing the challenge of information overload. The case study focuses on a formaldehyde production simulator and examines the performance of an improved Human-Machine Interface (HMI) with the use of an AI-based recommendation system utilizing a dynamic influence diagram in conjunction with reinforcement learning. The preliminary results indicate the potential of these methods to aid operators in decision-making during challenging situations and enhance process safety in the industry
Mietkiewicz, Joseph; Abbas, Ammar N.; Amazu, Chidera Winifred; Madsen, Anders L.; and Baldissone, Gabriele, "Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework and Application for Decision Support for Operators in Control Rooms" (2023). Articles. 533.
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