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Prediction of the behavior of C-4130 low alloy steel in HCl acid using artificial neural networks
Other Title(s)
التنبؤ في سلوك الصلب السبائكي في حامض الهيدروكلوريك C-4130
Dissertant
Thesis advisor
Comitee Members
Muhammad, Haydar Muadh
Hassan, Abd al-Karim F.
Hammud, Ali S.
University
University of Basrah
Faculty
Engineering College
Department
Department of Material Engineering
University Country
Iraq
Degree
Master
Degree Date
2012
English Abstract
Many engineering industrial equipmentsneed acid cleaning for washing mill scale or removing complex scale adherent to the metallic surfaces as one of the steps of the maintenance program.
But, severe corrosion problem may be arising if they improperly controlled. The aim of the present work is to study the effect of different environmental factors on the corrosion behavior of bare (non scale – containing surface) and scale – containing alloy steel samples using corrosion tests and pickling rate tests respectively.
A pharmaceutical compound as " Ampicillin + Cloxacillin " (ACX) were evaluated as corrosion inhibitor for alloy steel in acidic medium. The proposed artificial neural network (ANN) models using the experimental data obtained from corrosion test and pickling rate tests involved : 1) Prediction of corrosion rate and inhibition efficiency in the corrosive environment in the absence and presence of organic inhibitor (ACX) using bare metal sample.
The input variables were : the concentration of corrosive medium, concentration of ACX inhibitor, and immersion durations; while the output variables were the corrosion rate and inhibition efficiency. 2) Prediction of pickling rate in the corrosive environment in the absence and presence of ACX inhibitor using the scale - containing metallic surface of alloy steel samples.
The input variables are the concentration of corrosive medium, concentration of ACX inhibitor, and immersion durations; while the output variables were the scale density removed and dissolved iron content in acid medium. Back propagation networks were used for the proposed ANN models of the present work to find the targets of each model.
The prediction results of the two models of corrosion testand pickling rate showed good agreement with the obtained experimental values.
The correlation coefficients for the first and second models were found to be (R = 0.99936, R = 0.99542) respectively.
The mean square error for both models were found to be (MSE = 3.670 × 10-4, MSE = 1.302 × 10-3) respectively. It was concluded that ANN model can predict the corrosion parameters for any set of conditions.
The predicted results showed a decrease of corrosion rate and increase of inhibition efficiency with the increase of inhibitor concentration.
The results of pickling rate showed reliable prediction of the optimum concentration of inhibitor at the maximum acid concentration required for complete removal of scale.
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Topics
No. of Pages
104
Table of Contents
Table of contents.
Abstract.
Chapter One : introduction.
Chapter Two : literature review.
Chapter Three : experimental work.
Chapter Four : artificial neural networks.
Chapter Five : results and discussions.
Chapter Six : conclusions and recomondations.
References.
American Psychological Association (APA)
Abd al-Imam, Ula Habib. (2012). Prediction of the behavior of C-4130 low alloy steel in HCl acid using artificial neural networks. (Master's theses Theses and Dissertations Master). University of Basrah, Iraq
https://search.emarefa.net/detail/BIM-317179
Modern Language Association (MLA)
Abd al-Imam, Ula Habib. Prediction of the behavior of C-4130 low alloy steel in HCl acid using artificial neural networks. (Master's theses Theses and Dissertations Master). University of Basrah. (2012).
https://search.emarefa.net/detail/BIM-317179
American Medical Association (AMA)
Abd al-Imam, Ula Habib. (2012). Prediction of the behavior of C-4130 low alloy steel in HCl acid using artificial neural networks. (Master's theses Theses and Dissertations Master). University of Basrah, Iraq
https://search.emarefa.net/detail/BIM-317179
Language
English
Data Type
Arab Theses
Record ID
BIM-317179