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

页码：2364 / 2373

页数：10

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