Mechanism and data model of ethylene oxide reactors considering catalyst deactivation
Ethylene oxide(EO)is one of the important derivatives in the ethylene industry and is primarily produced by direct oxidation of ethylene using silver catalysts.Due to slow deactivation of catalysts and requirements of maintaining yield and high selectivity by adjusting operating conditions periodically,a method of coupling mechanism model and data model was proposed to determine the changes in catalyst activity and the impact of various operating conditions.Using Alibaba Cloud's Intelligent manufacturing platform to mine production data of an industrial ethylene oxide reactor,models of various operating conditions and reactor outlet composition were established using reaction kinetics,deactivation kinetics,reactor model,and support vector regression,respectively.The results show that after adding the activity variables obtained from the mechanism model to the input variables,the support vector regression model is more accurate than the original model,and the mean absolute percentage error of calculated reactor outlet composition and selectivity on the test set is less than 3%,which provides a basis for optimizing selectivity.
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