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

被引：16
作者：
Ahmadi, Mohammad Ali [1 ]
Rozyn, Jake [2 ]
Lee, Moonyong [3 ]
Bahadori, Alireza [2 ]
机构：
[1] PUT, Ahwaz Fac Petr Engn, Ahvaz, Iran
[2] So Cross Univ, Sch Environm Sci & Engn, Lismore, NSW 2480, Australia
[3] Yeungnam Univ, Sch Chem Engn, Gyongsan 712749, South Korea
来源：
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY | 2016年 / 35卷 / 02期
关键词：
steam; silica; boiler; predictive modeling; least-squares support vector machine; ARTIFICIAL NEURAL-NETWORK; ASPHALTENE PRECIPITATION; PREDICTION; BOILER; WATER; PERMEABILITY; PERFORMANCE; COMBUSTION; RESERVOIRS; ALGORITHM;
D O I：
10.1002/ep.12251
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
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 (R-2) 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. (c) 2015 American Institute of Chemical Engineers Environ Prog, 35: 596-602, 2016
引用
收藏
页码：596 / 602
页数：7
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