Chemical Methodologies
7.4 (Q1)
CiteScore2024
Q2
Web of Science

Prediction of Atmospheric Corrosion of Ancient Door Knockers via Neural Networks

Document Type : Original Article

Authors

Shahrzad Houshmandynia 1
Roya Raked 2
Fardad Golbabaei 3

1 Department of MBA - Marketing Islamic Azad University-Arak Branch, P.O. Box 1997683953, Arak, Iran

2 Department of masters of Handicrafts, Art and Architecture of Ardakan, Yazd, Iran

3 Department of Wood and Paper Science, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

https://doi.org/10.22034/chemm.2018.65388
Abstract
The importance of door knockers persuades us to anticipate the atmospheric corrosion through Neural Network (NN) which is validated by data originated from literature. NNs are used in order to anticipate the effective parameter on bronze atmospheric corrosion including the ambient temperature, exposition time, relative humidity, PH, SO2 concentration as an air pollutant and also metal’s precipitations. As these factors are extremely complicated, exact mathematical language of the diverse metals corrosion are not comprehended. The results of this study showed that SO2 concentration as an air pollutant and time of exposition are the fundamental effects on corrosion weight loss of bronze.

Graphical Abstract

Prediction of Atmospheric Corrosion of Ancient Door Knockers via Neural Networks

Keywords

Anticipation
Neural Network
atmospheric corrosion
Bronze corrosion

Subjects

References
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Chemical Methodologies
Volume 2, Issue 4 - Serial Number 4
Autumn 2018
Pages 324-332

  • Receive Date 14 May 2018
  • Revise Date 06 July 2018
  • Accept Date 06 July 2018

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