Developing supervised models for estimating methylene blue removal by silver nanoparticles

被引：25

作者：

Lariche, Milad Janghorban ^{[1
]}

Soltani, Shahrzad ^{[1
]}

Nezhad, Hossein Davoudi ^{[1
]}

Moradi, Hamed ^{[1
]}

Soltani, Sheyda ^{[1
]}

Farsayad, Fatemeh ^{[1
]}

Kazerouni, Hossein Moradi ^{[1
]}

机构：

[1] Abadan Sch Med Sci, Dept Med, Abadan, Iran

来源：

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS | 2020年 / 42卷 / 10期

关键词：

Methylene blue; LSSVM; silver nanoparticles; predicting model; wastewater; CONNECTIONIST MODEL; ACTIVATED CARBON; PREDICTION; EQUILIBRIUM; SOLUBILITY; PERFORMANCE; COMBUSTION; ADSORPTION; RESERVOIRS; EFFICIENCY;

D O I：

10.1080/15567036.2019.1602233

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Treatment of wastewater from synthetic constituents such as methylene blue (MB) which are extensively harmful to environment and human health is known as important issue in different industries. Also, implementing experiments to solve problems in engineering issues are highly time consuming and costly, so in the current work, the least square support vector machine (LSSVM) is coupled with two different evolutionary algorithms as particle swarm optimization (PSO) and genetic algorithm (GA) to predict removal MB on silver nanoparticles. The proposed algorithms have been evaluated with the experimental data graphically and statistically. The coefficients of determination for LSSVM-PSO and LSSVM-GA have been reported as 0.99999 and 0.99952, respectively. Also these methods have been compared with existing artificial neural network. The comparisons show that proposed methods have acceptable accuracy to predict MB extraction by silver nanoparticles.

引用

页码：1247 / 1254

页数：8

共 37 条

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