Estimation of wax deposition in the oil production units using RBF-ANN strategy. (2nd September 2017)

• Record Type: Journal Article
• Authors: Eghtedaei, Reza; Sasanipour, Jafar; Zarrabi, Houman; Palizian, Masih; Baghban, Alireza
• Is Part Of: Petroleum science and technology. Volume 35:Number 17(2017)
• Page Start: 1737
• Page End: 1742
• Publication Date: 2017-09-02
• Subjects: correlation -- levenberg marquardt -- oil production unit -- RBFANN -- wax deposition; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2017.1380043
• Languages: English
• ISSNs: 1091-6466

Abstract

ABSTRACT: Accurate prediction of the solid deposition is petroleum industry can result in increasing the production efficiency. This can also result in the elimination of a major industrial problem, namely the wax deposition. In this study, application of intelligent methods in prediction of the wax deposition is investigated by developing a radial basis function artificial neural network. Levenberg Marquardt algorithm is also applied to determine the optimum predictions. Results from the proposed model are also compared to Kamari et al. model revealing the better performance of the proposed RBF-ANN. The validity of the proposed model is also investigated using statistical and graphical approaches, illustrating the great capability of the proposed RBF-ANN in accurate prediction of the wax deposition. R-squared and mean squared error values of 0.9975 and 0.029251 are obtained for the proposed model, revealing the validity of the RBF-ANN in estimating the wax deposition.