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.