Development of an intelligent model for wax deposition in oil pipeline

Abstract

Crude oil transport is one important part of the oil industry. Wax deposition is a very complex phenomenon that in recent years is one of the major challenges in oil industry. Wax deposited on the inner surface of crude oil pipelines are capable to reduce or completely stop the oil flow and the oil industry imposing large costs. The main objective of this study was to present a novel approach for predication of wax deposition thickness in single-phase turbulent flow rate. Using experimental data set and Adaptive neural-fuzzy inference system (ANFIS) model was developed. From the results predicted by this model, it can be pointed out that the ANFIS model can be used as powerful tools for prediction of wax deposition thickness in single-phase turbulent flow rate with mean square error, absolute relative deviation error and average absolute deviation error which are 0.00077034, 0.015720 and 0.097961, respectively.