Robust Data Reconciliation in Supercritical Carbon Dioxide Thermal Systems: From Framework Design to Performance Evaluation

A number of studies have been carried out to analyze the theoretical performance of super critical carbon dioxide (S-CO2) systems, but monitoring its actual performance when gross errors appear in measurements can be quite challenging. This paper proposes a robust data reconciliation framework to cope with the gross errors happening in S-CO2 systems. A schematic S-CO2 recompression cycle was constructed with different types of measurement sensors, and various estimators with tuned parameters were evaluated to compare their performance. A hybrid strategy with two optimization solvers was designed to ensure the convergence of the solution. Results demonstrated the effectiveness of the proposed robust data reconciliation framework, where the mean relative error (MRE) of all measurements can be reduced from 1.02% to 0.39%, and the MRE of the gross errors can even be reduced from 4.79% down to only 1.11%. Statistics indicated that the Welsch estimator offered the best overall performance, while the Cauchy estimator proved to be more stable. The methods and conclusions provided in this paper can inspire subsequent research on data processing and the operation optimization of real S-CO2 systems.