Artificial neural network model to predict cold filter plugging point of blended diesel fuels |
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| Affiliation: | 1. School of Chemical Engineering and Technology, Tianjin University, 300072 Tianjin, P.R. China;2. College of Pharmaceuticals and Biotechnology, Tianjin University, 300072 Tianjin, P.R. China;3. Refinery of Beijing Yanshan Petrochemical Corporation, 102503 Beijing, P.R. China;1. Training Base of State Key Laboratory of Coal Science and Technology Jointly Constructed by Shanxi Province and Ministry of Science and Technology, Taiyuan University of Technology, Taiyuan 030024, PR China;2. Xi’An University of Science and Technology, Xi’an 710054, PR China;1. Turkish Land Forces NCO Vocational College, Automotive Sciences Department, 10110 Balikesir, Turkey;2. Ege University, Solar Energy Institute, 35100 Izmir, Turkey;1. Training Base of State Key Laboratory of Coal Science and Technology Jointly Constructed by Shanxi Province and Ministry of Science and Technology, Taiyuan University of Technology, Taiyuan 030024, PR China;2. Chinese Academy of Engineering, No. 2 Bing Jiao Kou HuTong, Beijing 100088, PR China;3. Shanghai Yankuang Energy Science and Technology Development Co., Ltd., Shanghai 201203, PR China;1. Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos CP 62209, Mexico;2. Instituto Mexicano del Petróleo, Lázaro Cárdenas 152, Col. San Bartolo Atepehuacan, México DF CP 07730, Mexico |
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| Abstract: | Diesel fuel blending is an indispensable process in the diesel fuel producing process. It will benefit greatly the refineries to increase their profits if a mathematic model is developed to accurately estimate CFPP instead of substantial experiments. In this article, a back propagation artificial neural network model is established to predict CFPP of the blended diesel fuels, using input parameters of kinematics viscosity, density, refractivity intercept, CFPP and weight percentages of constituent diesel fuels. This model can give satisfactory predicting results for unknown diesel fuel samples either without PPD or with PPD and has been tested by practical industrial applications of produce blended diesel fuels. The mean predicting errors for the unknown samples without PPD are about 1.3 °C and about 2.5 °C for unknown samples with PPD. |
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