Prediction of LOCA’s break size and location based on random forest and Multi Tasking Deep Neural Network

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This work presents an approach for predicting break size and location in nuclear power plants (NPP) loss of coolant accidents (LOCA) with or without emergency shutdown of a nuclear reactor (SCRAM). To accomplish that, a multi-tasking deep neural network (MTDNN) model is proposed. The training and validation data sets were generated on a PWR simulator at the Human-Systems Interface Laboratory (LABIHS) at Instituto de Engenharia Nuclear (IEN), Brazil. For each simulation, the time behaviors of more than a hundred state variables were recorded and a random forest (RF) algorithm was applied to select the most relevant variables. Using the set of 15 variables selected, the MTDNN model was trained and applied to a test set, in which accuracy of 97.26 % for location and relative error of about 3.77 were obtained for break sizes between 0 and 1200 cm2.