Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory
Background and Objective: Dust modeling can be considered as an appropriate tool for predicting future industrial dust and identifying pollutant emission control strategies. Perceptron (MLP) and radial base (RBF) neural networks were used as a means for predicting the outflow dust from the main coge...
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Mashhad University of Medical Sciences
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doaj-4fcc2925d7304ceeb4d94b6dec3b6c8d2020-11-25T03:01:41ZfasMashhad University of Medical SciencesPizhūhish dar Bihdāsht-i Muḥīṭ.2423-52022423-52022019-04-0151435210.22038/jreh.2019.38083.127713290Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factoryseyed saeed keykhosravi0Farhad Nejadkoorki1Mahmood Amintoosi2Graduate student, Department of Environmental Engineering, Yazd University of IranFaculty member of the Department of Environmental Engineering, Yazd University of IranAssistant Professor, Department of Computer Science, Faculty of Mathematics and, Hakim Sabzevari i University, Iran.Background and Objective: Dust modeling can be considered as an appropriate tool for predicting future industrial dust and identifying pollutant emission control strategies. Perceptron (MLP) and radial base (RBF) neural networks were used as a means for predicting the outflow dust from the main cogeneration of Sabzevar cement factory located in Khorasan Razavi Province.<br /> Method: the concentration of dust from the main cement chimney in the study area was measured through field measurements. Then, the parameters of the production line (temperature, speed of gas output, voltage, fuel, raw materials, and time of sampling) were used as input data to the nerve networks to predict the concentration of dust. The values obtained from the implementation of the models were compared with the results of field measurements as a superior model selection.<br /> Results: The analysis of figures and statistical parameters showed that the mean squared errors for the two MLP and RBF models were as much as 1.787 and 21.263, respectively, and the correlation coefficients were as much as 0.99693 and 0.95811, respectively, which indicates a lower error and greater correlation between the MLP and RBF model in predicting the concentration of dust.<br /> Conclusion: Because of the high ability of perceptron nervous networks to predict dust concentration, this model can be a convenient and fast solution to predict the amount of dust in the industry.http://jreh.mums.ac.ir/article_13290_bac9ac68f82b093251148de743473bd5.pdfcement factorydustartificial neural networksair pollution |
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DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
seyed saeed keykhosravi Farhad Nejadkoorki Mahmood Amintoosi |
spellingShingle |
seyed saeed keykhosravi Farhad Nejadkoorki Mahmood Amintoosi Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory Pizhūhish dar Bihdāsht-i Muḥīṭ. cement factory dust artificial neural networks air pollution |
author_facet |
seyed saeed keykhosravi Farhad Nejadkoorki Mahmood Amintoosi |
author_sort |
seyed saeed keykhosravi |
title |
Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory |
title_short |
Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory |
title_full |
Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory |
title_fullStr |
Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory |
title_full_unstemmed |
Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory |
title_sort |
estimation of artificial neural networks (mlp and rbf) accuracy in anticipation of the dust of the sabzevar cement factory |
publisher |
Mashhad University of Medical Sciences |
series |
Pizhūhish dar Bihdāsht-i Muḥīṭ. |
issn |
2423-5202 2423-5202 |
publishDate |
2019-04-01 |
description |
Background and Objective: Dust modeling can be considered as an appropriate tool for predicting future industrial dust and identifying pollutant emission control strategies. Perceptron (MLP) and radial base (RBF) neural networks were used as a means for predicting the outflow dust from the main cogeneration of Sabzevar cement factory located in Khorasan Razavi Province.<br /> Method: the concentration of dust from the main cement chimney in the study area was measured through field measurements. Then, the parameters of the production line (temperature, speed of gas output, voltage, fuel, raw materials, and time of sampling) were used as input data to the nerve networks to predict the concentration of dust. The values obtained from the implementation of the models were compared with the results of field measurements as a superior model selection.<br /> Results: The analysis of figures and statistical parameters showed that the mean squared errors for the two MLP and RBF models were as much as 1.787 and 21.263, respectively, and the correlation coefficients were as much as 0.99693 and 0.95811, respectively, which indicates a lower error and greater correlation between the MLP and RBF model in predicting the concentration of dust.<br /> Conclusion: Because of the high ability of perceptron nervous networks to predict dust concentration, this model can be a convenient and fast solution to predict the amount of dust in the industry. |
topic |
cement factory dust artificial neural networks air pollution |
url |
http://jreh.mums.ac.ir/article_13290_bac9ac68f82b093251148de743473bd5.pdf |
work_keys_str_mv |
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