Development of multicriteria nuclear dismantling management incorporated with fuzzy deep learning in nuclear power plants (NPPs)

Abstract

Abstract The nuclear dismantling assessment is analyzed with the fuzzy set and deep‐learning algorithm, where the project preference time search (PPTS) has been constructed. Artificial intelligence (AI) management is applied to nuclear decommissioning for the best estimation. The basic data are from three factors, Licensee, NRC, and Public. In the analysis, the highest OUTPUT value is 0.0513151 in the 72nd year, which is the best time for decommissioning, because the confidence value in the 37th year is 78.76% compared to that of the 72nd year. In the other case of membership functions with right‐shift values, the change rate is higher in the 72nd year as being similar to the value in the 37th year near 0.09 in final confidence value. The trend is a new function that shows two peaks compared to the previous one. The other cases could be made by comparing in the interested time. Finally, the list of reactor decommissions processes with numbering is used to find out the very confident time using the final confidence value as the PPTS method.

Keywords

• deep‐learning
• dismantling
• fuzzy
• nuclear decommissioning
• power