Intelligent prediction of aliphatic and aromatic hydrocarbons in Caspian Sea sediment using a neural network based on particle swarm optimization

被引：4
|
作者：
Amin, Javad Sayyad [1 ,2 ]
Kuyakhi, Hossein Rajabi [1 ]
Bahadori, Alireza [3 ]
机构：
[1] Univ Guilan, Dept Chem Engn, Rasht, Iran
[2] Univ Guilan, Caspian Sea Basin Res Ctr, Dept Marine Ind, Rasht, Iran
[3] Southern Cross Univ, Sch Environm Sci & Engn, Lismore, NSW, Australia
来源：
PETROLEUM SCIENCE AND TECHNOLOGY | 2019年 / 37卷 / 24期
关键词：
anthropogenic; Caspian Sea; feed forward artificial neural; organic pollutants; particle swarm optimization; ASPHALTENE PRECIPITATION; CARBON-DIOXIDE; SURFACE SEDIMENTS; PAHS; COASTAL; BAY;
D O I：
10.1080/10916466.2018.1542439
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：
In this paper an intelligent model is proposed to predict the amount of organic pollutants in Caspian Sea sediment based on a feed forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) algorithm. Organic pollutants have carcinogenesis and mutagenesis properties which are derived from anthropogenic and natural sources. The PSO-ANN was developed by experimental data collected from different literature. The statistical parameters prove the satisfactory performance of the proposed PSO- ANN model. A good correlation was obtained between the predicted organic pollutants and the experimental data for test, train and validation data were 0.996, 0.997, 0.993, respectively.
引用
收藏
页码：2364 / 2373
页数：10
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