Data-driven modeling of fireside corrosion rate

Abstract

Purpose – This paper aims to propose an efficient artificial neural network (ANN) model using multi-layer perceptron philosophy to predict the fireside corrosion rate of superheater tubes in coal fire boiler assembly using operational data of an Indian typical thermal power plant. Design/methodology/approach – An efficient gradient-based network training algorithm has been used to minimize the network training errors. The input parameters comprise of coal chemistry, namely, coal ash and sulfur contents, flue gas temperature, SOX concentrations in flue gas, fly ash chemistry (Wt.% Na2O and K2O). Findings – 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 superheater boiler tubes have been computationally investigated and parametric sensitivity analysis has been undertaken. Originality/value – 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.