Modeling Fireside Corrosion Rate in a Coal Fired Boiler Using Adaptive Neural Network Formalism

In this paper, an efficient artificial neural network (ANN) model using multi-layer perceptron (MLP) philosophy has been proposed to predict the fireside corrosion rate of super heater tubes in coal fire boiler assembly, using operational data of an Indian typical thermal power plant. The input parameters comprise coal chemistry, namely, coal ash and sulfur contents, flue gas temperature, SOX concentrations in flue gas, fly ash chemistry (wt% Na2O and K2O). An efficient gradient based network training algorithm has been employed to minimize the network training errors. Effects of coal ash and sulfur contents, wt% of Na2O and K2O in fly ash and operating variables such as flue gas temperature and percentage excess air intake for coal combustion on the fireside corrosion behavior of super heater boiler tubes have been computationally investigated and parametric sensitivity analysis has been undertaken. It has been observed that ash and sulfur contents of coal, flue gas temperature and fly ash chemistry have a relatively predominant influence on the rate of fireside corrosion with respect to other parameters. Quite good agreement between ANN model predictions and the measured values of fireside corrosion rate has been observed, which is corroborated by the regression fit between these values. © 2016, Sociedade Portuguesa de Electroquimica. All rights reserved. Modeling Fireside Corrosion Rate in a Coal Fired Boiler Using Adaptive Neural Network Formalism. Available from: https://www.researchgate.net/publication/303782462_Modeling_Fireside_Corrosion_Rate_in_a_Coal_Fired_Boiler_Using_Adaptive_Neural_Network_Formalism [accessed Aug 16, 2017]