Nonlinear state estimation of a power plant superheater by using the extended Kalman filter for differential algebraic equation systems

•Developed first-principles dynamic model of an industrial superheater/reheater.•Developed and applied a nonlinear estimator algorithm for DAE system.•Compared model and estimator results with dynamic data from an operating plant.•Estimator yielded considerably superior results for all variable that are compared.•Estimator performance also evaluated for load-following operation by the power plant. A nonlinear estimator was developed for process monitoring of a power plant superheater system to estimate the transient temperature profiles of the critical measured and unmeasured variables under load-following operations. The model used in the estimator was a detailed, distributed, first-principles, differential–algebraic equation model. The superheater geometry is characterized based on an operating natural gas combined cycle power plant. The model included mass and energy balances for the operating fluids, i.e., steam and flue gas, with due consideration of tube wall temperature dynamics. The dynamic model was used for state and parameter estimation by using a modified extended Kalman filter. When results are compared with the data from a natural gas power plant, the estimator yielded root mean squared error of 0.2 °C for the main steam temperature compared to 1 °C obtained from the first-principles model. While the first-principles model yielded as high as 2.5 °C absolute instantaneous error for the tube temperatures, the estimator reduced the absolute instantaneous error below 0.3 °C for most variables. Performance of the estimator for estimating critical variables like the main steam temperature and flue gas temperature was evaluated in the presence of high model mismatch and noisy measurement data by simulating load-following operation of the plant. The estimator yielded maximum absolute instantaneous error of 3.4 °C for the flue gas and 1 °C for the main steam temperature.