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العنوان
Energy Conservation on Petroleum Refineries :
المؤلف
Gomaa, Mohamed Samy Saad.
هيئة الاعداد
باحث / محمد سامى سعد جمعة
مشرف / حسن عبد المنعم فرج
مشرف / حسن على حسن
مناقش / ايهاب جابر ادم
مناقش / شريف رمضان هدارة
الموضوع
Mechanical Engineering.
تاريخ النشر
2016.
عدد الصفحات
74 p. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الميكانيكية
تاريخ الإجازة
1/6/2016
مكان الإجازة
جامعة الاسكندريه - كلية الهندسة - الهندسة الميكانيكية
الفهرس
Only 14 pages are availabe for public view

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from 97

Abstract

The petroleum refining industry in Egypt is one of the most important industries in our country, providing inputs to any economic sector, including the transport sector and the chemical industry. Energy use is also a major source of emissions in the refinery industry making energy efficiency improvement an attractive opportunity to reduce emissions and operating costs.Governmental Programs aim to reduce the energy subsides by increasing oil and gas production by optimizing oil refineries process and increasing the end users efficiency.This paper addresses the first step in optimization of oil refineries process by simulating the VDU (Vacuum Distillation Unit) of which receives variable assay from CDU (Crude Distillation Unit) residue from an adjacent refinery. The success key is to attain the requested demand. Steady state models continue to be powerful and efficient tools for the optimization of the distillation columns. This paper presents a VDU Steady state models procedure and an example application of this technique to an actual column.Due to the uncertain quality of the feed the modeling and optimization will be very difficult so we must define a good way for this challenge for creating a suitable simulation for the refineries. VDU simulation has this trouble which has undefined feed. ASPEN HYSYS was used as the engine of the simulation and a lot of challenges were found to achieve the Vacuum Distillation Unit VDU simulation, but at the end the result is the successful technique for uncertain feed VDU to achieve the requested demand. Ready for the future research to perform the optimization using the Genetic algorithm and neural analysis.