During nuclear accidents, decision-makers need to handle considerable data to take appropriate protective actions to protect people and the environment from radioactive material release. In such scenarios, machine learning can be an essential tool in facilitating the protection action decisions that will be made by decision-makers. By feeding machines software with big data to analyze and identify nuclear accident behavior, types, and the concentrations of released radioactive materials can be predicted, thus helping in early warning and protecting people and the environment. In this study, based on the ground deposition concentration of radioactive materials at different distances offsite in an emergency planning zone (EPZ), we proposed classification and regression models for three severe accidents. The objective of the classification model is to recognize the transient situation type for taking appropriate actions, while the objective of the regression model is to estimate the concentrations of the released radioactive materials. We used the Personal Computer Transient Analyser (PCTRAN) Advanced Power Reactor (APR) 1400 to simulate three severe accident scenarios and to generate a source term released to the environment. Additionally, the Radiological Consequence Analysis Program (RCAP) was used to assess the off-site consequences of nuclear power plant accidents and to estimate the ground deposition concentrations of radionuclides. Moreover, ground deposition concentrations at different distances were used as input data for the classification and regression tree (CART) models to obtain an accident pattern and to establish a prediction model. Results showed that the ground deposition concentration at a near distance from a nuclear power plant is a more informative parameter in predicting the concentration of radioactive material release, while the ground deposition concentration at a far distance is a very informative parameter in identifying accident types. In the regression model, the R-square of the training and test data was 0.995 and 0.994, respectively, showing a mean strong linear relationship between the predicted and actual concentration of radioactive material release. The mean absolute percentage error was found to be 26.9% and 28.1% for the training and test data, respectively. In the classification model, the model predicted a scenario (1) of 99.8% and 98.9%, scenario (2) of 98.4% and 91.6%, and scenario (3) of 98.6% and 94.7% for the training and test data, respectively.
In June 2021, the United States (US) Department of Energy (DOE) hosted the first-ever Hydrogen Shot Summit, which lasted for two days. More than 3000 stockholders around the world were convened at the summit to discuss how low-cost clean hydrogen production would be a huge step towards solving climate change. Hydrogen is a dynamic fuel that can be used across all industrial sectors to lower the carbon intensity. By 2030, the summit hopes to have developed a means to reduce the current cost of clean hydrogen by 80%; i.e., to USD 1 per kilogram. Because of the importance of clean hydrogen towards carbon neutrality, the overall DOE budget for Fiscal Year 2021 is USD 35.4 billion and the total budget for DOE hydrogen activities in Fiscal Year 2021 is USD 285 million, representing 0.81% of the total DOE budget for 2021. The DOE hydrogen budget of 2021 is estimated to increase to USD 400 million in Fiscal Year 2022. The global hydrogen market is growing, and the US is playing an active role in ensuring its growth. Depending on the electricity source used, the electrolysis of hydrogen can have no greenhouse gas emissions. When assessing the advantages and economic viability of hydrogen production by electrolysis, it is important to take into account the source of the necessary electricity as well as emissions resulting from electricity generation. In this study, to evaluate the levelized cost of nuclear hydrogen production, the International Atomic Energy Agency Hydrogen Economic Evaluation Program is used to model four types of LWRs: Exelon’s Nine Mile Point Nuclear Power Plant (NPP) in New York; Palo Verde NPP in Arizona; Davis-Besse NPP in Ohio; and Prairie Island NPP in Minnesota. Each of these LWRs has a different method of hydrogen production. The results show that the total cost of hydrogen production for Exelon’s Nine Mile Point NPP, Palo Verde NPP, Davis-Besse NPP, and Prairie Island NPP was 4.85 ± 0.66, 4.77 ± 1.36, 3.09 ± 1.19, and 0.69 ± 0.03 USD/kg, respectively. These findings show that, among the nuclear reactors, the cost of nuclear hydrogen production using Exelon’s Nine Mile Point NPP reactor is the highest, whereas the cost of nuclear hydrogen production using the Prairie Island NPP reactor is the lowest.
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