A Fast Dynamic Optimization Framework for Modular High-Temperature Gas-Cooled Reactors Power Plants: An Integrated asMHE-asNMPC Approach

Controlling nonlinear dynamic systems poses significant challenges in process systems engineering (PSE) due to their complex nonlinear dynamics, time-varying parameters, and vulnerability to external disturbances. This study proposes a robust real-time control framework that synergistically integrates Moving Horizon Estimation (MHE) with Nonlinear Model Predictive Control (NMPC) to address these challenges in practical engineering applications. To enhance computational efficiency and mitigate online delays, an advanced-step strategy—comprising advanced-step NMPC (asNMPC) and advanced-step MHE (asMHE)—is introduced, leveraging offline precomputation and online correction. The proposed methodology demonstrates significant potential for controlling nuclear energy systems, particularly in managing the intricate dynamics of the High-Temperature Gas-Cooled Reactor Pebble-bed Module (HTR-PM). Two representative control scenarios were designed for the NSSS module to experimentally validate the framework. The results showcase superior tracking accuracy, robust disturbance rejection, and enhanced real-time computational efficiency, affirming the approach’s effectiveness and resilience in highly nonlinear environments with stringent real-time demands. This work provides both theoretical insights and practical guidance for controlling complex industrial processes, such as those in nuclear energy systems, advancing the field of nonlinear control.