Pressurizer Control Optimization with Deep Learning-Based Predictions

With a consideration of alleviating the unstable control responses in traditional pressurizer control, this work adopts the cutting-edge deep learning method to optimize the PID control performance. A Long Short-Term Memory (LSTM) model is trained by data from a traditional PID control simulation and is then used to provide predictions to the PID controller such that the newly constructed intelligent controller can produce control signals for real time working conditions. The verification experiments conducted for both functionality and complex inputs successfully proved the advantages of the intelligent controller, showing up to 80.7