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Date of Publication:2018-01-01

Journal:化工学报

Volume:69

Issue:3

Page Number:1053-1063

ISSN No.:0438-1157

Abstract:As an important raw material for chemical production, the demands of ethylene greatly increase, but it consumes large energy. Since ethylene production and operation status is directly related to the level of energy efficiency, the economic benefits of enterprises are affected. It is great significance to realize the intelligent identification of ethylene production operating conditions for saving energy and reducing consumption. Therefore, a comprehensive method for the abnormity identification in ethylene production is presented by using the IPSO-optimized SVM-BOXPLOT method based on the key energy efficiency indicators, ethylene yield, propylene yield and comprehensive energy consumption. Specifically, the data dimensionality is reduced on the basis of the deep analysis of the ethylene production technology and the data analysis. Then the working conditions are classified by SVM for reducing the scope of abnormal recognition. Finally, the abnormal data is identified by BOXPLOT. Combined with the on-line monitoring system, the scheme is applied to the production of a petrochemical enterprise. The monitoring and diagnosis scheme for abnormal working conditions has higher model precision and faster convergence speed. The method not only realizes the monitoring and diagnosis of abnormal working conditions in ethylene production, but also meets the technological requirements of actual operating conditions, which ensures the real-time and accuracy of abnormal identification.

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