Computerized Operator Support Systems to Aid Decision Making in Nuclear Power Plants

Boring, Ronald L.; Thomas, Kenneth D.; Ulrich, Thomas A.; Lew, Roger

doi:10.1016/j.promfg.2015.07.604

Cited by 33 publications

(12 citation statements)

References 4 publications

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“…Some recent examples of automated modernization within nuclear operations include the Computerized Operator Support System [26] and computer-based procedures [27]. The Computerized Operator Support System is a performance aid for operators with process algorithms and fault detection that together provide an advisory system and decision recommendations.…”

Section: Trust In Automation In Nuclear Operationsmentioning

confidence: 99%

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

Agarwal

Walker

Manjunatha

et al. 2022

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The research and development reported here is part of the Technology Enabled Risk-Informed Maintenance Strategy project sponsored by the U.S. Department of Energy's Light Water Reactor Sustainability program. The primary objective of the research presented in this report is to produce a technical basis for developing explainable and trustable artificial intelligence (AI) and machine learning (ML) technologies. The technical basis will lay the foundation for addressing the technical and regulatory adoption challenges of AI/ML technologies across plant assets and the nuclear industry at scale and to achieve seamless cost-effective automation without compromising plant safety and reliability.The technical basis ensuring wider adoption of AI/ML technologies presented in this report was developed by Idaho National Laboratory (INL), in collaboration with Public Service Enterprise Group (PSEG) Nuclear, LLC. To develop the initial technical basis, the circulating water system (CWS) at the PSEG-owned plant sites was selected as the identified plant asset. Specifically, the issue of waterbox fouling diagnosis in the CWS using different types of CWS data is presented to address the said challenge. The approach presented in this report is based on the closed-loop forward-backward process that tries to capture the advancements in data science addressing the explainability of AI/ML outcomes, user-centric interpretability of those outcomes, and how user interpretation can be used as feedback to further simplify the process. A prototype interface is developed to present a focused component-level display of the ML model outputs in a usable and digestible form.

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Section: Trust In Automation In Nuclear Operationsmentioning

confidence: 99%

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

Agarwal

Walker

Manjunatha

et al. 2022

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“…A probabilisticbased decision-making tool integrated into the supervisory control system is proposed in [195] which could make real-time decision-making precisely and has been tested in advanced multi-modular reactors. Furthermore, a concept of computerized advisory operator support system based on predefined functions for each functional part is introduced to assist operators in detecting, monitoring, and so on [196]. With the application of dynamic event trees and deep learning, some researchers [197], [198] proposed an on-line operator supporting tool that enables the capability of emergency planning in real-time.…”

Section: ) Decision-making Assistancementioning

confidence: 99%

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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Nuclear energy can make an important contribution to low-carbon energy supply for Industry 4.0, while Industry 4.0 can reform this industry in return. As a typical and complex man-machine-network integration system, various faults, insufficient automation and stressed human operators limit the further popularization of nuclear power plants (NPPs) while these issues can be addressed by the aid of artificial intelligence (AI) technologies. In this work, we try to present a systemic review of how AI can benefit NPPs in a top-to-down fashion. We discuss limitations in current NPPs and introduce the concept of Nuclear Power Plant Human-Cyber-Physical System (NPPHCPS) as the top-level design. Then, we category AI-related nuclear power applications into Physical-Plant-Centered and Human-Operator-Centered technologies and review research works from 7 typical NPP functional scenarios in the recent two decades. In each NPP functional scenario, how researchers integrate AI into NPPs is presented following timeline. We hope this review can be used as the guideline for NPPs' Design in the future and contribute to green Industry 4.0.

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“…The preliminary hybrid energy systems interface builds on general design findings from previous efforts designing Computerized Operator Support Systems for a chemical volume and control system of a nuclear power plant (Boring, Thomas, Ulrich, & Lew 2015;Lew, Ulrich, & Boring, 2017). The layout is comprised of two monitors (see Figure 2).…”

Section: Rancor Microworld Simulatormentioning

confidence: 99%

Rancor Hybrid Energy System Microworld

Lew

Ulrich²,

Boring

2020

Proceedings of the Human Factors and Ergonomics Society Annual

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Transitioning from fossil fuels to renewable energy sources will require a diverse energy portfolio to ensure a reliable and resilient electrical grid. Renewable sources are proliferating but are intermittent and low periods of low production must be offset by other energy generators. When renewable demand is high baseload generators must scale back or utilize energy for other sources such as hydrogen production. Hybrid energy systems such as nuclear thermolysis hydrogen production could play a critical role for our energy future. Hydrogen is critical for manufacturing fertilizer as well as other industrial processes. Here we describe the development of a human system interface for a micro-reactor thermal storage hydrogen production system. The development of the interface parallels the engineering of a physical test-loop known as the Thermal Energy Delivery System (TEDS) at Idaho National Laboratory.

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Computerized Operator Support Systems to Aid Decision Making in Nuclear Power Plants

Cited by 33 publications

References 4 publications

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Rancor Hybrid Energy System Microworld

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