Application of ANN to predict the apparent viscosity of waxy crude oil

This study investigated the apparent viscosity of waxy crude oil treated with pour point depressant (PPD). It considered the shear history and thermal history as the main factors to affect the apparent viscosity of the crude oil when transported by a long-distance pipeline. According to previous practice and present laboratory works, the apparent viscosity that can be determined according to the conventional test specifications without taking into account the effect of shear history cannot be used to successfully design and operate a waxy crude oil pipeline. Thus, with the help of entropy generation (sg) combination, which is caused by the viscous flow of crude in the pipeline and the back propagation artificial neural networks (ANN) optimized by a genetic algorithm, a prediction model was developed to determine the viscosity of PPD treated waxy crude oil, which was affected by shear. The performance of the model was evaluated through four statistical indices, such as the Mean Absolute Percentage Error (MAPE). The MAPE of all data of the apparent viscosity was 12.20%. The influence of each variable on the apparent viscosity was investigated through a sensitivity analysis, which revealed that sg caused by viscous flow rates was the most important variable in viscosity prediction.