Neural network modeling of the fouling in crude oil preheat exchangers

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.

کلیدواژه ها:

Crude oil-Fouling-Modeling-Neural Network