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:: Volume 6, Issue 1 (Winter 2017 2017) ::
Arch Hyg Sci 2017, 6(1): 96-104 Back to browse issues page
Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process
Hossein Jafari Mansoorian , Mostafa Karimaee , Mahdi Hadi , Elaheh Jame Porazmey , Farzan Barati , Mansour Baziar
Department of Environmental Health Engineering, School of Health, Tehran University of Medical Science, Tehran, Iran
Abstract:   (895 Views)

Background & Aims of the Study: A feed forward artificial neural network (FFANN) was developed to predict the efficiency of total petroleum hydrocarbon (TPH) removal from a contaminated soil, using soil washing process with Tween 80. The main objective of this study was to assess the performance of developed FFANN model for the estimation of   TPH removal.

Materials and Methods: Several independent repressors including pH, shaking speed, surfactant concentration and contact time were used to describe the removal of TPH as a dependent variable in a FFANN model. 85% of data set observations were used for training the model and remaining 15% were used for model testing, approximately. The performance of the model was compared with linear regression and assessed, using Root of Mean Square Error (RMSE) as goodness-of-fit measure

Results: For the prediction of TPH removal efficiency, a FANN model with a three-hidden-layer structure of 4-3-1 and a learning rate of 0.01 showed the best predictive results. The RMSE and R2 for the training and testing steps of the model were obtained to be 2.596, 0.966, 10.70 and 0.78, respectively.

Conclusion: For about 80% of the TPH removal efficiency can be described by the assessed regressors the developed model. Thus, focusing on the optimization of soil washing process regarding to shaking speed, contact time, surfactant concentration and pH can improve the TPH removal performance from polluted soils. The results of this study could be the basis for the application of FANN for the assessment of soil washing process and the control of petroleum hydrocarbon emission into the environments.

Keywords: Artificial neural network, Modeling, TPH, Surfactant, Soil contamination, Iran
Full-Text [PDF 752 kb]   (329 Downloads)    
Type of Study: Research | Subject: Environmental Health
Received: 2016/06/5 | Accepted: 2016/12/29 | Published: 2017/01/1
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Jafari Mansoorian H, Karimaee M, Hadi M, Jame Porazmey E, Barati F, Baziar M. Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process. Arch Hyg Sci. 2017; 6 (1) :96-104
URL: http://jhygiene.muq.ac.ir/article-1-161-en.html
Volume 6, Issue 1 (Winter 2017 2017) Back to browse issues page
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