Research on fuzzy adaptive GPC of average coolant temperature based on regulation of boric acid concentration

Abstract

In order to keep the balance of power distribution in the core of a nuclear reactor, the method of adjusting the boric acid concentration to regulate the average temperature of the coolant has been adopted to track the demands of power changes in the core. However, the chemical reaction process of regulating the average coolant temperature by adjusting the boric acid concentration is too slow to better meet the demands of power changes. In view of this challenge, this paper proposes an innovative fuzzy adaptive incremental generalized predictive control strategy to optimize the process of regulating the coolant temperature. This strategy employs a dynamic adjustment of control parameters, specifically through the application of incremental factors γ, which is calibrated using a fuzzy logic-based algorithm to enhance system responsiveness and robustness in real-time. The strategy aims to improve the efficiency and responsiveness of the nuclear reactor control mechanism and further enhance the safety and stability of nuclear power generation.Firstly, the system state space equations are established based on the boric acid concentration equation and the nuclear reactor kinetics. Secondly, the step factors β and incremental factors γ are used in improved-GPC to improve control real-time and robustness. The step factor β speeds up the rolling optimization, and the incremental factor γ is adjusted based on the fuzzy algorithm according to the online recognition degree of the system. Finally, the 1000 MW Pressurized Water Reactor is modeled and simulated. The results show that improved-GPC has a shorter regulation time and a stronger ability to cope with the model mismatch in this system than other controllers.