Application of bayesian regularized artificial neural networks to predict pour point of crude oil treated by pour point depressant

• Published in: Petroleum science and technology Vol. 35; no. 13; pp. 1349 - 1354
• Main Authors: Hu, Kai, Zhang, Fan, Wang, Shengjie, Zhang, Yuyao, Zhang, Yaqin, Liu, Kai, Gao, Qian, Meng, Xiaoyu, Meng, Jiayan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.07.2017; Taylor & Francis Ltd
• Subjects: Ammonia; Artificial neural networks; Bayesian analysis; BRANN; Crude oil; Mathematical models; network performance; Neural networks; pour point; PPD-treatment crude oil; Repair & maintenance; Sensitivity analysis; Viscous flow
• ISSN: 1091-6466; 1532-2459
• DOI: 10.1080/10916466.2017.1330346

The pour point of the crude oil treated with the pour point depressant (PPD) is easily affected by the shear history effect. Models for pour point of PPD-treatment crude oil affected by the shear history effect based on Bayesian regularized artificial neural network (BRANN) were established. The results showed that BRANN models not only had a good ability of fitting to the training data, but also had a good ability of predicting the testing data. By evaluating network performance with several statistical indicators, the three models have excellent performance, high accuracy, and strong generalization ability. The influence of each parameter on the pour point were also investigated through a sensitivity analysis, which shows that the entropy generation due to viscous flow is the most important parameter in predicting the pour point.