A deep learning-based framework for the operation prediction of primary heat transfer loop in nuclear power plants

Abstract

A deep learning-based multi-node framework is constructed in this work to provide a data-driven platform that provides predictions for the operation condition of the primary heat transfer (PHT) loop in nuclear power plants (NPPs). Several deep learning models that have been verified and demonstrated in previous researches, such as Long-Short Term Memory (LSTM), Convolutional Neural Network (CNN), and zigmoid-based LSTM (zLSTM), are applied to modeling critical system parameters at three important nodes in the PHT loop. The feature extraction and process memory are enhanced via the collaborative work of CNN and LSTM. zLSTM, on the other hand, is successfully utilized to strengthen the long-term memory, especially for predictions of a node with multivariate inputs such as the steam generator. The node prediction results are also adopted for a polynomial fitting that generates an additional input to the next node, allowing each node to select a more accurate input. According to the verification experiments based on Loss of Coolant Accident (LOCA), the Mean Squared Error (MSE) result (1.29 × 10−3) and the Mean Absolute Error (MAE) result (1.37 × 10−2) of 0.7 cm2 LOCA case demonstrate the functionality and accuracy of the proposed framework. It is found that the fitting error (MSE) in the outlet node at 0.7 cm2 case is 38.5% lower than the prediction, showing the advantage of applying both deep learning and fitting methods. The best performance, in term of MSE, is obtained at SG node in the 0.7 cm2 case, where its processing error (0.001285) is 93.2% lower than that of the baseline models. Both the validation and verification experiments successfully proved the feasibility and advantages of the proposed framework, which offers an alternative option for the operation analysis of PHT performance.