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 2021

 نمذجة وتحسين خشونة السطح لطلاء مركب الإيبوكسي / الجسيمات النانوية المستخدمة كطبقة واقية في مجاري المداخن

 Abualsaud, Jaber Mohammed Jaber


//uquui/handle/20.500.12248/131944
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نمذجة وتحسين خشونة السطح لطلاء مركب الإيبوكسي / الجسيمات النانوية المستخدمة كطبقة واقية في مجاري المداخن

عناوين أخرى : Modelling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating Used as Protective Layer in Flue Ducts
رقم الطلب : 24532
الناشر :جامعة أم القرى
مكان النشر : مكة المكرمة
تاريخ النشر : 2021 - 1442 هـ
الوصف : 56 ورقة
نوع الوعاء : ماجستير
اللغة : انجليزي
المصدر : مكتبة الملك عبدالله بن عبدالعزيز الجامعية
يظهر في المجموعات : الرسائل العلمية المحدثة

In power plants, flue gases cause a severe corrosion damage in its metallic parts such as flue duct, heat exchanger and boiler. Coating still one of the effective techniques to eliminate his damage. A robust fuzzy model of the surface roughness profile such average roughness; Ra and average of ten-point height; Rz of flue gas duct coated by protective composite coating from epoxy and Nano-particles was built based on the experimental data set. The proposed model consists of four different Nano-particles (ZnO, ZrO2, SiO2 and NiO) with 2%, 4%, 6%, and 8%. Response surface methodology (RSM) and Fuzzy logic system approach were used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct. In order to prove the superiority of the proposed fuzzy model, the model results are compared with those obtained by ANOVA. The coefficient of determination and the root mean square error (RMSE) are used as metrics of the comparison. For Ra, for the first output response, using ANOVA, the coefficient of determination values is 0.9137 for training value and 0.4037 for prediction value. Similarly, for Rz the second output response, the coefficient of determination results is 0.9695 and 0.4037, respectively, for training and prediction. In the Fuzzy modelling of Ra: the first output response, the RMSE values are 0.0 and 0.1455, respectively, for training and testing. The values for the coefficient of determination are 1.00 and 0.9807, respectively, for training and testing. The obtained results proved the superiority of fuzzy modelling. For modelling the second output response Rz, the RMSE values are 0.0 and 0.0421respectively for training and testing and the coefficient of determination values are 1.00 and 0.9959, respectively for training and testing. The observed R^2 value, adjusted R^2 , predicted R^2 , and F-values indicate that the developed

العنوان: نمذجة وتحسين خشونة السطح لطلاء مركب الإيبوكسي / الجسيمات النانوية المستخدمة كطبقة واقية في مجاري المداخن
عناوين أخرى: Modelling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating Used as Protective Layer in Flue Ducts
المؤلفون: Mohamed, Korrany
Ahmed, Fathi
Abualsaud, Jaber Mohammed Jaber
الموضوعات :: الهندسة الميكانيكية
تاريخ النشر :: 2021
الناشر :: جامعة أم القرى
الملخص: In power plants, flue gases cause a severe corrosion damage in its metallic parts such as flue duct, heat exchanger and boiler. Coating still one of the effective techniques to eliminate his damage. A robust fuzzy model of the surface roughness profile such average roughness; Ra and average of ten-point height; Rz of flue gas duct coated by protective composite coating from epoxy and Nano-particles was built based on the experimental data set. The proposed model consists of four different Nano-particles (ZnO, ZrO2, SiO2 and NiO) with 2%, 4%, 6%, and 8%. Response surface methodology (RSM) and Fuzzy logic system approach were used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct. In order to prove the superiority of the proposed fuzzy model, the model results are compared with those obtained by ANOVA. The coefficient of determination and the root mean square error (RMSE) are used as metrics of the comparison. For Ra, for the first output response, using ANOVA, the coefficient of determination values is 0.9137 for training value and 0.4037 for prediction value. Similarly, for Rz the second output response, the coefficient of determination results is 0.9695 and 0.4037, respectively, for training and prediction. In the Fuzzy modelling of Ra: the first output response, the RMSE values are 0.0 and 0.1455, respectively, for training and testing. The values for the coefficient of determination are 1.00 and 0.9807, respectively, for training and testing. The obtained results proved the superiority of fuzzy modelling. For modelling the second output response Rz, the RMSE values are 0.0 and 0.0421respectively for training and testing and the coefficient of determination values are 1.00 and 0.9959, respectively for training and testing. The observed R^2 value, adjusted R^2 , predicted R^2 , and F-values indicate that the developed
الوصف :: 56 ورقة
الرابط: http://dorar.uqu.edu.sa//uquui/handle/20.500.12248/131944
يظهر في المجموعات :الرسائل العلمية المحدثة

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ملف الوصف الحجمالتنسيق 
24532.pdf
"   الوصول المحدود"
الرسالة الكاملة1.58 MBAdobe PDFعرض/ فتح
طلب نسخة
abse24532.pdf
"   الوصول المحدود"
ملخص الرسالة بالإنجليزي83.34 kBAdobe PDFعرض/ فتح
طلب نسخة
cont 24532.pdf
"   الوصول المحدود"
فهرس الموضوعات206.64 kBAdobe PDFعرض/ فتح
طلب نسخة
titel24532.pdf
"   الوصول المحدود"
غلاف9.86 kBAdobe PDFعرض/ فتح
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