Design of data-driven model for the pressurizer system in nuclear power plants using a TSK fuzzy neural network - Mahmoud, Tarek A.; Sheta, Amal A.; Fikry, Refaat M.; Ali, Elsayed H.; El-Araby, Sayed M.; Mahmoud, Mohammed I. (Nuclear engineering and design, 2022)
•Developing a TSKFNN to model the pressurizer unit in PWRs.•Using the fuzzy C means and the kernel ridge regression to initiate and define the TSKFNN structure.•Developing the BP and the kernel RLS to update the TSKFNN model parameters.•Investigate the capability of the TSKNN model using different data sets from the PCTRAIN simulator.•Comparing the developed TSKFNN model with two mathematical models. In pressurized water reactors (PWRs), the main role of the pressurizer unit is to maintain the primary circuit pressure of the reactor within preset limits. To ensure this role, the pressure and the water level control systems for the pressurizer system are very significant. Hence, it is very important to monitor and predict them with great accuracy during operation. In this paper, a data-driven model using a TSK fuzzy neural network (TSKFNN) for the prediction of the pressure and the water level inside the pressurizer is developed. The identification task of the TSKFNN model of the pressurizer system is fulfilled in two steps: the structure and the parameters identification. In the first step, the fuzzy C-means clustering method is used to separate the input data into clusters and obtain the rule antecedent parameters of the TSKFNN model. Besides, the initial values of the rule consequent parameters are defined using the kernel ridge regression algorithm. In the second step, the sliding-window Kernel Recursive Least Squares (KRLS) algorithm and the gradient method are developed to adapt TSKFNN model parameters. By using input/ output data collected from the PCTran VVER-1200 simulator, the data-driven model was trained, tested, and evaluated. The simulation results demonstrate that the data-driven model using the TSKFNN structure can effectively predict the pressure and the water level in the pressurizer system. Furthermore, the simulation results also involve a comparison with two other mathematical models to confirm the effectiveness of the developed data-driven model.