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- W2761560078 abstract "A targeted approach to soot blow operation based on boiler operating conditions involves estimation of cleanliness factor. The present work introduces a way of estimating the cleanliness factor of thermal power plant reheater by artificial neural network (ANN). Three different algorithms used to train NN and their performance is compared. Based on the simulation studies it was observed that although the Levenberg-Marquardt algorithm is having better performance than EKF and Scaled Conjugate Gradient (SCG) in training, the EKF algorithm has the best accuracy in the testing period. Sequential zeroing of weights (SZW), a sensitivity analysis is also performed to identify the relative significance of the input variables. This analysis showed that flue gas inlet and outlet temperature have greater influence on cleanliness factor." @default.
- W2761560078 created "2017-10-20" @default.
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- W2761560078 date "2017-01-01" @default.
- W2761560078 modified "2023-09-24" @default.
- W2761560078 title "Estimation of reheater cleanliness factor based on Kalman filtering method in neural network training" @default.
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- W2761560078 doi "https://doi.org/10.1109/tima.2017.8064799" @default.
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