Estimation of the silica solubility in the superheated steam using LSSVM modeling approach

Abstract

The presence of silica (SiO2) in boiler water causes precipitation and creates hard silicate scale on steam turbine blades. This study assessed the ability of least squares support vector machines (LSSVM) modeling approaches to estimate the solubility of SiO2 in the steam of boilers. A genetic algorithm (GA) and population-based stochastic search algorithms were employed to identify the optimal LSSVM method variables. Results indicate that the GA-LSSVM can be used to model the complicated nonlinear relationship between the input and output variables. To predict the solubility of SiO2 in the steam of boilers, the GA-LSSVM model generated the mean absolute error (MAE) and the coefficient of determination (R2) values of 1.8831 and 0.9997, respectively, for the entire data set. The proposed model provides a distinctly promising approach to estimating the solubility of SiO2 in the steam of boilers