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A solution for the nuclear waste problem is the key challenge for an extensive use of nuclear reactors as a major carbon free, sustainable, and applied highly reliable energy source. Partitioning and Transmutation (P&T) promises a solution for improved waste management. Current strategies rely on systems designed in the 60’s for the massive production of plutonium. We propose an innovative strategic development plan based on invention and innovation described with the concept of developments in s-curves identifying the current boundary conditions, and the evolvable objectives. This leads to the ultimate, universal vision for energy production characterized by minimal use of resources and production of waste, while being economically affordable and safe, secure and reliable in operation. This vision is transformed into a mission for a disruptive development of the future nuclear energy system operated by burning of existing spent nuclear fuel (SNF) without prior reprocessing. This highly innovative approach fulfils the sustainability goals and creates new options for P&T. A proof on the feasibility from neutronic point of view is given demonstrating sufficient breeding of fissile material from the inserted SNF. The system does neither require new resources nor produce additional waste, thus it provides a highly sustainable option for a future nuclear system fulfilling the requests of P&T as side effect. In addition, this nuclear system provides enhanced resistance against misuse of Pu and a significantly reduced fuel cycle. However, the new system requires a demand driven rethinking of the separation process to be efficient.

Abstract:The current generation of nuclear reactors are evolutionary in design, mostly based on the technology originally designed to power submarines, and dominated by light water reactors. The aims of the Generation IV consortium are driven by sustainability, safety and reliability, economics, and proliferation resistance. The aims are extended here to encompass the ultimate and universal vision for strategic development of energy production, the "perpetuum mobile"-at least as close as possible. We propose to rethink nuclear reactor design with the mission to develop an innovative system which uses no fresh resources and produces no fresh waste during operation as well as generates power safe and reliably in economic way. The results of the innovative simulations presented here demonstrate that, from a theoretical perspective, it is feasible to fulfil the mission through the direct reuse of spent nuclear fuel from currently operating reactors as the fuel for a proposed new reactor. The produced waste is less burdensome than current spent nuclear fuel which is used as feed to the system. However, safety, reliability and operational economics will need to be demonstrated to create the basis for the long term success of nuclear reactors as a major carbon free, sustainable, and applied highly reliable energy source.

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Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN. On the other hand, using cross-validation to select the best performing ANN based on the ANN with the highest R value can lead to biassing in the prediction. This is as a result of the fact that the use of R cannot determine if the prediction made by ANN is biased. Additionally, R does not indicate if a model is adequate, as it is possible to have a low R for a good model and a high R for a bad model. Hence, in this paper, we propose an approach to improve the robustness of a prediction made by ANN. The approach is based on a systematic combination of identical trained ANNs, by coupling the Bayesian framework and model averaging. Additionally, the uncertainties of the robust prediction derived from the approach are quantified in terms of confidence intervals. To demonstrate the applicability of the proposed approach, two synthetic numerical examples are presented. Finally, the proposed approach is used to perform a reliability and sensitivity analyses on a process simulation model of a UK nuclear effluent treatment plant developed by National Nuclear Laboratory (NNL) and treated in this study as a black-box employing a set of training data as a test case. This model has been extensively validated against plant and experimental data and used to support the UK effluent discharge strategy.