Asset management of ageing nuclear power plant systems - Hadri, Omar (2022)
Even though nuclear energy is largely considered a form of clean energy from the point of view of CO2 emissions, it poses several environmental and public safety challenges due to radioactive activities and wastes. To ensure safe operation, nuclear power plants must be designed and operated to guarantee that the reactor power is controlled, the fuel is cooled, and that radioactivity is always contained. Currently, there are around 450 commercial nuclear reactors operating in 30 countries generating around 10% of the world’s electricity production. Almost 60% of operating plants were built more than 30 years ago; which is the design-life time of a nuclear plant. However, current operational policies and programmes are not generally suitable to analyse the activities necessary for a nuclear power plant to meet additional extended operational life. Therefore, several countries have been developing license renewal frameworks to extend the operational licenses of their nuclear plants to meet the growing demand for energy. Nowadays, a probabilistic risk assessment (PRA) is commonly used to analyse the safety of a nuclear plant and as a tool for risk-informed decision-making. Nevertheless, current PRA techniques do not properly account for the effect of components ageing, which has the potential to increase the risk of operating a nuclear plant. In this project a PRA framework is developed to address the shortcoming of existing PRA models. The proposed framework relies on a modularised extension to Petri-nets model and offers higher modelling capabilities. The advantage of the new framework is that it can be easily adapted to a wide range of complex mechanical systems and can analyse the performance of systems under different asset management policies. Moreover, to address the challenge of modelling component degradation, this project has presented a degradation approach that can combine continuous and discrete degradation models. This resulted in a more realistic representation of component degradation. Finally, the project investigated the challenge of complex system optimisation and presented a machine-learning based optimisation technique that can reduce optimisation time considerably.