An intelligent model for predicting wax deposition thickness during turbulent flow of oil. (18th August 2017)

• Record Type: Journal Article
• Authors: Saeedi Dehaghani, Amir Hossein
• Is Part Of: Petroleum science and technology. Volume 35:Number 16(2017)
• Page Start: 1706
• Page End: 1711
• Publication Date: 2017-08-18
• Subjects: artificial neural network -- fuzzy inference system -- intelligent model -- turbulent flow -- wax deposition; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2017.1358281
• Languages: English
• ISSNs: 1091-6466

Abstract

Abstract: To mitigate wax deposition, having an accurate model is of vital importance. In this study, an artificial neural network (ANN), with 19 and 8 neurons at its hidden layers, was developed to predict wax deposition thickness (WDT) during single-phase turbulent flow of oil. The proposed ANN takes wax content, Reynolds number, oil/pipeline temperature, and deposition time as input arguments. Predicted WDT by ANN was in close agreement with experimental data, with AARD% and RMS of 4.5369 and 0.011, respectively. Prediction of ANN was compared with that of adaptive neuro-fuzzy inference system (ANFIS). Results demonstrate superiority of ANN over ANFIS.