Prediction of the behavior of C-4130 low alloy steel in HCl acid using artificial neural networks

العناوين الأخرى

التنبؤ في سلوك الصلب السبائكي في حامض الهيدروكلوريك C-4130

مقدم أطروحة جامعية

Abd al-Imam, Ula Habib

مشرف أطروحة جامعية

Hammadi, Nawal J.

أعضاء اللجنة

Muhammad, Haydar Muadh
Hassan, Abd al-Karim F.
Hammud, Ali S.

الجامعة

جامعة البصرة

الكلية

كلية الهندسة

القسم الأكاديمي

قسم هندسة المواد

دولة الجامعة

العراق

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2012

الملخص الإنجليزي

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.

التخصصات الرئيسية

العلوم الهندسية والتكنولوجية (متداخلة التخصصات)

الموضوعات

• الخواص الميكانيكية
• الشبكات العصبية(الحاسبات الإلكترونية)
• سبائك الصلب

عدد الصفحات

104

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-317179