Research Advances in Measurement and Prediction Models of Crude#br#
Oil Fouling Rate Under High Temperature

doi:10.11885/j.issn.1674-5086.2013.11.21.04

Abstract

Abstract:

A comprehensive review in aspect of laboratory measurement and prediction models is conducted in this paper，
and more focus is put on the analysis and development of prediction models. Both experiments and prediction models play
significant role in determining crude oil fouling rate under high temperature. As a quick access to vast amounts of valid data，
laboratory experiments，either batch stirred or recirculation test apparatus cannot directly guide fouling prevention in production.
Proper models must reflect the chemical nature of fouling，the significance of influencing factors such as physical properties，
temperature，flow rate and other different process. Although the threshold models have gained great achievements in field，
which are semi-empirical formula，the precision and applicability of most prediction models at present are limited. More
accurate models must be based on further studies on fouling mechanism. Meanwhile，according to its successful application to
description of the characters of nonlinear systems，the ANN（artificial neural network）will be a new promising prediction skill.

Key words: crude oil fouling, fouling rate, experiments, prediction models, ANN

CLC Number:

TE624.2

YUAN Zongming1, WANG Yong1*, XIE Ying1, WANG Dapeng2. Research Advances in Measurement and Prediction Models of Crude#br# Oil Fouling Rate Under High Temperature[J]. 西南石油大学学报(自然科学版), DOI: 10.11885/j.issn.1674-5086.2013.11.21.04.

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Research Advances in Measurement and Prediction Models of Crude#br# Oil Fouling Rate Under High Temperature

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