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

核能作为一种碳足迹小的未来全球能源具有巨大的潜力。为了实现这一潜力，安全（以及公众对安全的看法）至关重要，无论是现有的还是新设计的核电站，都在努力提高安全性，保持可用性，降低运行和维护成本。此外，核电站寿命的延长和电力的更新推动了对诊断和预测核电站健康状况的新工具的需求。通过多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对关键变量在线监测的判断，并不总是可靠的。这个项目将联合三所英国大学和一家印度核电站，直接解决在开发核电站在线监测工具方面的建模、验证和核查变化。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，找到准确预测可能发生的不幸事件的最小数据需求，以及设计实验以最大化数据的信息内容。然后，我们将使用优化后的系统预测冷却剂可能流失的事故，并确定具体地点，之后，我们将进一步预测各种事故情况下可能发生的放射性释放，为了便于应急准备，我们将对释放后阶段进行建模，以预测所考虑的情况下的扩散模式。最后，所有的模型都将被验证、验证并集成到一个工具中，该工具可用于监测和充当早期预警设备，以防止此类情况的发生。

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.