Koushiki Mukhopadhyay
Strathclyde Business School
Department of Management Science
Purpose – The current global scenario witnesses various threats to the ecosystem, such as depletion of resources, environmental pollution, and global warming. Such a situation of endangerment necessitates the increasing dependence towards sustainable alternatives, one example of which is nuclear energy. Nuclear energy is a form of zero-emission clean energy without harmful byproducts, which purifies the air quality and protects our health. Data gathered by the Nuclear Energy Institute shows that nuclear power had helped the United States avoid more than 476 million metric tons of Carbon Dioxide emissions in 2019.
To meet worldwide energy needs, it is crucial to develop appropriate maintenance policies for the existing 440 nuclear reactors across the globe, with the aim of extending their operating life (Ramuhalli et al.,2012). Optimal maintenance decisions are achievable by collecting continuous accurate information about the health of nuclear systems. This information will help to estimate the remaining useful life of the systems at given time points, which influences the maintenance policies. This information is collected by sensors connected to the systems. However, due to ageing effects and their extreme working environments, these sensors often deteriorate, providing erroneous information about the system's health condition, leading to the formulation of sub-optimal decisions. This research aims to develop a model that will determine the degradation rate of the sensors at each time point, which will consequently help estimate the system's health accurately.
Methodology – The research will mostly depend on various quantitative methods such as Stochastic modelling and Bayesian estimation techniques. Though, in the future, interviews will be planned to gain a better understanding of the background.
Implications – The existing models in previous research works have not studied the sensor degradation effect in-depth, which is the novelty of this research. The models developed will not only benefit the nuclear systems but will also have the flexibility to be applied in other engineering systems, such as for the maintenance of wind turbines and hydroelectric power generators. Thus, developing such robust decision support models that support sustainable and renewable energy sources aligns well with Goal 7: Affordable and Clean Energy of the United Nations' Sustainable development goals, having significance for society.
References
Pradeep Ramuhalli, Jamie Coble, Ryan M Meyer, and Leonard J Bond. Prognostics health management and life beyond 60 for nuclear power plants. In 2012 Future of Instrumentation International Workshop (FIIW) Proceedings, pages 1–4. IEEE, 2012.
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