Constrained Nonlinear Model Predictive Control for High Temperature Gas-Cooled Reactor in Load Following

Abstract As the climate issue has become the focus of global attention, new energy with less carbon emissions have attracted the attention of the world, especially nuclear energy. High temperature gas-cooled reactor is an inherently safe reactor designed by the Institute of Nuclear and New Energy Technology of Tsinghua University and has been put into commercial operation. The nuclear steam supply system (NSSS) is the key system of the modular high temperature gas-cooled reactor for generating superheated steam, including a reactor core and a once-through steam generator (OTSG). In the process of nuclear power plant operation, power-level control is the key technology to ensure the NSSS to operate in the desired state. By adjusting and controlling the control rod speed, the rotation-rate of the main helium circulator and the valve-opening, the neutron flux, helium temperature and water supply flow rate are adjusted, and then the power-level maintenance, maneuvering and load tracking are realized. Considering the strong nonlinearity of NSSS, it is necessary to develop nonlinear power-level control methods. In this paper, model predictive control (MPC) is proposed to realize load following in the NSSS of high temperature gas-cooled reactor. MPC has the advantage of obtaining control input by iterating and solving optimization problems, which has a good application scenario for power-level control of nuclear power plants. The research in this paper has an enlightening significance for the application of MPC to the power-level control of high temperature gas-cooled reactor. In the future engineering application, facing the actual situation of multi-objective and multi-constraint conditions, the MPC iterative optimization idea can be used to obtain the control input that can make the nuclear power plant stable and meet the operation requirements.