Steam generator efficiency optimization under uncertainty through multi-fidelity modeling

Abstract

The steam generator is an essential equipment in power plants, and understanding its behavior with the aid of mathematical models allows for improved decision-making aiming toward better efficiency. Well-calibrated models can help identify new operating conditions that lead to efficiency enhancement. Unfortunately, due to the complexity of steam generators, computationally efficient models based solely on energy-mass balance usually present limiting simplifications. Alternatively, computational fluid dynamics can solve reacting flow phenomena but require a great amount of computational time. Purely data-driven models often extrapolate poorly as they do not necessarily contain the physical equations that drive the system’s behavior. In this scenario, the multi-fidelity approach is an attractive solution because it combines physics-grounded representation with a low computational footprint and allows for mapping the efficiency of the steam generator considering uncertainty propagation. This paper presents a multi-fidelity model that simulates the efficiency of a steam generator from the PECÉM power plant. The model is assembled by the sum of two Gaussian processes, trained using one high-fidelity and one low-fidelity dataset. The high-fidelity dataset is formed by the observed operation of the actual power plant at varying operating conditions. These same operating parameters are the input for the energy-mass balance model built using the EBSILON software to generate the low-fidelity dataset. The observed efficiency of the PECÉM power plant ranged from 82.89% to 84.37%, with a mean value of 83.58%. The resulting multi-fidelity model presents a root mean square error of 0.19%, which is six times lower than the error obtained by the energy-mass balance model. This model is then used for global sensitivity analysis and robust optimization. A Pareto front mapping expected efficiency and its confidence level is built by exploring not yet experimented operational points. Finally, data clustering allowed the identification of efficiencies around 86%, which adds 1.63% to the power plant’s maximum level.