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Soft-Sensing of carbon content of the catalyst in FCC based on deep learning

Xilei Wang  Ning Li  Shaoyuan Li  
【摘要】:In this paper,a novel modeling method of soft sensing is presented to measure the spent catalyst's carbon content in fluidized-bed catalytic cracking.This model focuses on improving the existing soft sensing models' performances,such as higher measurement accuracy,more robust working performance,or better generalization ability.To build this model,firstly,we choose the primary variables and secondary variables by analyzing the mechanism model of fluidized-bed catalytic cracking.Secondly,we build a 4 layers deep belief networks as a feature extraction structure to extract feature data from the input data.And then we build an improved least squares support vector regression which is optimized by artificial bee colony algorithm as the regression structure.The extracted feature data will be the input to the regression structure.Thirdly,we use the catalyst related production data of 2# fluidized-bed catalytic cracking device from Sinopec Jiujiang Branch Corporation to train the model.The simulation results verify the effectiveness of the presented method.
【作者单位】:Department of Automation and Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai Jiao Tong University
【基金】:supported by the National Nature Science Foundation of China(61374109,61590925) the National Basic Research Program of China(973Program-2013CB035500) National High Technology R&D Program of China(863 Program-2015AA043102) Shanghai Science and Technology Commission(NO.15dzl206700)
【分类号】:TE624.91;TP18
【正文快照】:
I.INTRODUCTION Theprocessofcrackingandcondensingof hydrocarbon of raw oil under thermic effect and catalyst conditionsiscalledcatalyticcracking,andFCC(Fluidized-bedCatalyticCracking)is widely used at present.The catalytic cracking is one of the princ

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