Document Type

Conference Paper

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

Mietkiewicz, J., Abbas, A.N. & Amazu, C.W. (2023). Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework and Application for Decision Support for Operators in Control Rooms. ESREL2023: European Safety and Reliability conference, University of Southampton, UK, September 3-8.

doi:10.3850/978-981-18-8071-1_P531-cd

Abstract

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.

DOI

https://doi.org/10.3850/978-981-18-8071-1_P531-cd

Funder

European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement no. 955901

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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