Smart on-line monitoring for nuclear power plants (SMART)

核电站智能在线监测（SMART）

• 批准号: EP/M018717/1
• 负责人: Jiamei Deng
• 金额: $ 38.35万
• 依托单位: Leeds Beckett University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM018717%2F1
• 关键词: Smart; line; monitoring; nuclear; power

Nuclear power has great potential as a future global power source with a small carbon footprint. To realise this potential, safety (and also the public perception of safety) is of the utmost importance, and both existing and new design nuclear power plants strive to improve safety, maintain availability and reduce the cost of operation and maintenance. Moreover, plant life extensions and power updates push the demand for the new tools for diagnosing and prognosing the health of nuclear power plants. Monitoring the status of plants by diverse means has become a norm. Current approaches for diagnosis and prognosis, which rely heavily on operator judgement on the basis of online monitoring of key variables, are not always reliable. This project will bring together three UK Universities and an Indian nuclear power plant to directly address the modelling, validation and verification changes in developing online monitoring tools for nuclear power plant. The project will use artificial intelligence tools, where mathematical algorithms that emulate biological intelligence are used to solve difficult modelling, decision making and classification problems. This will involve optimizing the number of inputs to the models, finding the minimum data requirement for accurate prediction of possible untoward events, and designing experiments to maximize the information content of the data. We will then use the optimised system to predict potential loss of coolant accidents and pinpoint their specific locations, after which we will progress to prediction of possible radioactive release for various accident scenarios, and, in order to facilitate emergency preparedness, the post release phase will be modelled to predict the dispersion pattern for the scenarios under consideration. Finally, all of the models will be validated, verified and integrated into a tool that can be used to monitor and act as an early warning device to prevent such scenarios from occurring.

核能作为未来的全球能源具有巨大的潜力，碳足迹小。为了实现这一潜力，安全（以及公众对安全的看法）至关重要，现有和新设计的核电站都在努力提高安全性，保持可用性并降低运营和维护成本。此外，核电厂寿命延长和电力更新推动了对诊断和监测核电厂健康状况的新工具的需求。通过各种手段监测植物状况已成为一种常态。目前的诊断和预后方法在很大程度上依赖于操作员根据在线监测关键变量的判断，并不总是可靠的。该项目将汇集三所英国大学和一家印度核电厂，直接解决核电厂在线监测工具开发过程中的建模、验证和验证变化。该项目将使用人工智能工具，其中模拟生物智能的数学算法用于解决困难的建模，决策和分类问题。这将涉及优化模型的输入数量，找到准确预测可能的不利事件的最低数据要求，并设计实验以最大限度地提高数据的信息含量。然后，我们将使用优化的系统来预测潜在的冷却剂损失事故，并确定其具体位置，之后，我们将继续预测各种事故情景下可能的放射性释放，为了便于应急准备，将模拟释放后阶段，以预测所考虑情景的扩散模式。最后，将对所有模型进行验证、核实，并将其纳入一个工具，用于监测和充当预警装置，以防止发生此类情况。

项目成果

Selecting a minimum training set for neural networks using short time Fourier transform in detecting loss of coolant accidents in nuclear power plants

使用短时傅立叶变换检测核电厂冷却剂损失事故的神经网络选择最小训练集

• 发表时间: 2018
• 作者: D. Tian

Failure Modes Detection of Nuclear Systems Using Machine Learning

• DOI: 10.1109/dsa.2018.00017
• 发表时间: 2018-09
• 期刊: 2018 5th International Conference on Dependable Systems and Their Applications (DSA)
• 作者: David Tian;Jiamei Deng;E. Zio;F. Maio;Fu-cheng Liao

,A constraint-based random search algorithm for optimizing neural network architecture and ensemble construction in detecting loss of coolant accidents in nuclear power plants

,一种基于约束的随机搜索算法，用于优化神经网络架构和集成结构，用于检测核电厂冷却剂丢失事故

• 发表时间: 2018
• 作者: D. Tian

A Fuzzy Controller for Fault Tolerant Control of Nuclear Reactor

• DOI: 10.1109/csci.2017.64
• 发表时间: 2017-12
• 期刊: 2017 International Conference on Computational Science and Computational Intelligence (CSCI)
• 作者: M. Wang;Jiamei Deng;C. Pattinson;Suzanne Richardson

ANN Based Sensor and Actuator Fault Detection in Nuclear Reactors

核反应堆中基于神经网络的传感器和执行器故障检测

• DOI: 10.1109/iccma51325.2020.9301579
• 发表时间: 2020
• 作者: Banerjee S