سال انتشار: ۱۳۸۵

محل انتشار: یازدهمین کنگره ملی مهندسی شیمی ایران

تعداد صفحات: ۹

نویسنده(ها):

Javad Aminian – Department of Chemical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Shahrokh Shahhosseini – Assistant Professor in chemical engineering- Simulation and Control of Processes.

چکیده:

Crude oil fouling behavior in industrial preheat exchangers have been extensively studied by various researchers. However, a general, effective and robust method for prediction of crude oil fouling has yet been demanded. The objective of this paper is to develop and validate a robust Artificial Neural Network (ANN) model for predicting crude oil fouling process in industrial shell and tube heat exchangers. A comparison
between the model developed in this work and Panchal model employing reported experimental data revealed that the overall mean relative error obtained in this research was 89.6% lower.