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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of Petroleum Exploration and Production Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.