【摘要】 环氧乙烷是乙烯工业的重要衍生物之一，主要由乙烯在银催化剂作用下直接氧化生产。针对催化剂活性缓慢衰减，需定期调整操作条件以维持产量和高选择性的问题，提出了机理模型和数据模型耦合的方法，以确定催化剂的活性变化和各操作条件的影响。以某工业环氧乙烷反应器为对象，通过阿里云智能制造平台挖掘生产数据，分别使用反应动力学、失活动力学、反应器模型和支持向量回归，建立了各操作条件与反应器出口组成的模型。结果表明，在输入变量中添加机理模型所得的活性变量后，支持向量回归模型较原始模型更加准确，计算的反应器出口组成和选择性在测试集上的平均绝对百分比误差小于3%，为优化选择性提供了基础。更多还原

【Abstract】 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.更多还原