Recent research led by Xiaoyu Li from the School of Electrical Engineering and Automation at Xiamen University of Technology has introduced a novel method for optimizing the liquid level control in steam generators, which are essential components of nuclear power plants. This study, published in ‘Engineering Proceedings’, addresses a critical issue in the operation of steam generators: maintaining the appropriate water level. A significant deviation from the desired level can lead to equipment failures, affecting production efficiency and safety.
Traditionally, the process of adjusting the control parameters for steam generators has relied heavily on engineers’ experience and historical data. This approach can be costly and inefficient, often resulting in suboptimal performance. Li’s research proposes a hybrid iterative model reconstruction method that combines process data with advanced modeling techniques to enhance the control system’s performance.
The innovative approach involves creating an initial dataset using a small-sample Latin-square experiment design. From this dataset, two fitting models—Support Vector Machine (SVM) and Kriging—are constructed. The optimization process is driven by a particle swarm optimization algorithm, which identifies the optimal control parameters while dynamically updating the models based on real-time data during the iteration process. This method not only improves efficiency but also reduces the final iteration values by 13.57% and 16.27% compared to traditional single-model optimization methods.
Li emphasizes the significance of this research, stating, “By continuously updating the modeling dataset with process data generated during the iteration process, we can establish more accurate models in a reconstructed manner.” This capability allows for a more adaptive and self-learning control system, which is crucial for the highly nonlinear and time-varying characteristics of steam generator operations.
The implications of this research extend beyond just improved operational efficiency. As nuclear power remains a vital component of the global energy mix, optimizing steam generator performance can lead to increased reliability and safety, which are paramount for public trust and regulatory compliance. Additionally, the ability to leverage data-driven optimization techniques opens up new commercial opportunities for energy companies looking to enhance their operational frameworks and reduce costs.
This study not only contributes to the academic field but also provides a practical solution for the energy sector, particularly in the context of nuclear power plant commissioning and operation. As the industry continues to seek innovations that enhance performance while ensuring safety, Li’s work stands out as a promising advancement in steam generator control technology.
