Study on Vacuum Dehydration Rate from Oil Based on T_S Fuzzy Identifying Model

doi:10.16182/j.issn1004731x.joss.201701005

Abstract

Abstract: The process of vacuum dehydration from oil is time-varying, nonlinear, and difficult to be specified with mathematical methods. Takagi-Sugeno (T_S) fuzzy model of vacuum dehydration rate of oil purifier is proposed, which a method of applying Fuzzy C-Means (FCM) clustering algorithm and using the least square method identifying the consequent parameters. The nonlinear mapping is set up from four influence factors (the initial water content, the vacuum pressure , the initial temperature and running time) to vacuum dehydration rate using the T_S fuzzy model. The simulation and experimental results show the T_S model reflects the laws of the influences of initial moisture content on dehydration rate is larger, running time is a more optimal value, there is a monotonous variation trend of the influences of vacuum pressure and temperature on dehydration rate, and the model has preferable learning capabilities, which performs effectively in predicting vacuum dehydration rate．

Key words: vacuum dehydration, dehydration rate, FCM, T_S fuzzy identification, optimization

CLC Number:

TM6
TP18

Liu Ge, Chen Bin, Zhang Xianming. Study on Vacuum Dehydration Rate from Oil Based on T_S Fuzzy Identifying Model[J]. Journal of System Simulation, 2017, 29(1): 27-33.

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