다중회귀분석을 이용한 원전해체 방사성폐기물 발생량 예측 식 개발에 관한 연구
A Study on the Development of a Predictive Model for the Amount of Radioactive Waste from Nuclear Power Plant Decommissioning Using a Multi-regression Analysis
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In order to reduce the cost of radioactive waste disposal and to manage it efficiently,it is necessary to estimate the amount of radioactive decommissioning waste from nuclear powerplant. In this paper a predictive model was developed by a multi-regression analysis using Rstatistics package with reference to previous research. Decommissioning waste amount data for12 pressurized water reactors, 4 pressurized heavy water reactors and 3 boiling water reactorswere collected for a sample of multi-regression model. In regression analysis, amount of estimatedradioactive decommissioning waste was set as the dependent variable, and design net capacity,effective age, decommissioning strategy were selected as independent variables through threevariable selection methods. Test statistics: determination coefficient, adjusted determinationcoefficient, p-value, variance inflation factor of regression analysis were checked to verifysignificance of derived predictive model. It is expected that a more accurate predictive model canbe developed by obtaining additional decommissioning data of nuclear power plants.
Abstract
In order to reduce the cost of radioactive waste disposal and to manage it efficiently,it is necessary to estimate the amount of radioactive decommissioning waste from nuclear powerplant. In this paper a predictive model was developed by a multi-regression analysis using Rstatistics package with reference to previous research. Decommissioning waste amount data for12 pressurized water reactors, 4 pressurized heavy water reactors and 3 boiling water reactorswere collected for a sample of multi-regression model. In regression analysis, amount of estimatedradioactive decommissioning waste was set as the dependent variable, and design net capacity,effective age, decommissioning strategy were selected as independent variables through threevariable selection methods. Test statistics: determination coefficient, adjusted determinationcoefficient, p-value, variance inflation factor of regression analysis were checked to verifysignificance of derived predictive model. It is expected that a more accurate predictive model canbe developed by obtaining additional decommissioning data of nuclear power plants.
참고문헌 (19)
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