NDLI: Neural Modeling and Experimental Investigation of the Erosion Characteristics of Boiler Grade Steels Impacted by Fly Ash

Content Provider	Springer Nature Link
Author	Das, S. K. Mandal, Durbadal Sahoo, K. L.
Copyright Year	2015
Abstract	In this study, experimental investigations on solid particle erosion have been carried out for two typical boiler grade steels used for fabricating boiler components, namely, 0.22C steel and 1.25Cr-Mo-V steel. The erosion tests were conducted using an air jet erosion testing facility conforming to ASTM Standard G76 international testing method. Impingement angle of the air-particle stream on the target material was varied from 30° to 75°, and the particle impact velocity was also varied from 40 to 150 m/s. The surface morphology of the eroded samples was investigated by SEM to elucidate the erosion mechanism under different particle impact conditions. Further, an artificial neural network (ANN) model has been developed to predict the erosion behavior of these boiler grade steels impacted by boiler fly ash for more realistic characterization of erosion potential using pertinent data. An efficient network training optimization algorithm has been employed for faster convergence and better predictions. The ANN predictions of erosion rate are found to be in excellent agreement with the actual measured data as reported in the literature. It has been observed from the neural model results that the erosion potential of actual boiler fly ash with various levels of silica content is considerably higher than the synthetic erodent (alumina particles) usually used in the experiments to mimic fly ash erosion behavior under simulated conditions.
Starting Page	3513
Ending Page	3526
Page Count	14
File Format	PDF
ISSN	10599495
Journal	Journal of Materials Engineering and Performance
Volume Number	24
Issue Number	9
e-ISSN	15441024
Language	English
Publisher	Springer US
Publisher Date	2015-07-14
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	boiler-grade steel erosion erosion mechanism fly ash microstructure neural network model surface morphology Characterization and Evaluation of Materials Tribology, Corrosion and Coatings Quality Control, Reliability, Safety and Risk Engineering Design
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Materials Science Mechanical Engineering

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