催化重整反应38集总动力学模型及其在连续催化重整中的应用  

A 38-lumped kinetic model for reforming reaction and its application in continuous catalytic reforming

作　　者：王连山[1] 张泉灵[1] 梁超[1] 

机构地区：[1]浙江大学智能系统与控制研究所,浙江杭州310027

出　　处：《化工学报》2012年第4期1076-1082,共7页CIESC Journal

基　　金：国家高技术研究发展计划项目(2009AA043203;2009AA04Z162)~~

摘　　要：根据集总理论和催化重整的反应机理,基于工业连续重整装置,提出了一个包含38个集总组分、86个反应催化重整反应动力学模型。该模型将重整物料按碳原子数集总为C6～C11+组分,相同碳原子数的物料又划分为正构烷烃、异构烷烃、五元环烷烃、六元环烷烃和芳烃,裂化产物细分为C1～C5组分。通过合理简化,确定了86个待估模型参数,并在工业现场数据的基础上,利用分层策略与BFGS算法对其进行了估计。通过对某炼厂连续重整反应器的模拟计算对该模型进行了验证,计算值与实际值吻合较好,表明该模型具有较好的可靠性与准确性,达到了工业应用的要求。将模型用于芳烃收率的预测,在较大的时间跨度内,精度与趋势均令人满意。最后,利用该模型对芳烃收率进行了优化计算,经优化后芳烃收率提高0.17%,该结果可为连续重整装置的优化操作提供参考。Based on the lumping theory and catalytic reforming reaction mechanisms, a new kinetic model involving 38 lumped components and 86 reactions was developed for the industrial continuous catalytic reforming （CCR）. In the proposed model the reactants were lumped into C6-C11＋ according to the number of carbon atoms, the components with the same carbon atoms number were divided into normal paraffin, iso paraffin, 5-cyclanes, 6-cyclanes and aromatics respectively, and the cracking products were lumped into C1-C5 lumps. The reaction network of the model was on the basis of the reaction mechanism of bi-functional catalyst and related kinetic theory. As the detailed division of the lumped components, most of the reactants information could be covered, consequently the model could be more realistic. By making reasonable simplification, 86 model parameters were identified and estimated using the hierarchical strategy and BFGS algorithm from literature data. The model developed was used for simulation of a refinery reformer to validate the effectiveness. The prediction error of each component in the outlet products of the last reactor was within 0.7~, and that of temperature drop for each reactor was below 5℃. So, the reliability and accuracy of the model could meet the requirements of industrial applications. The modelwas then applied to predict the aromatics yield, and the average prediction error was only 0.42% The result showed that the trend and precision of the aromatics yield prediction were satisfied in a long period of time. Finally, the process optimization was implemented based on the proposed model, and an increase of 0.17% on aromatics yield was obtained by a slight tuning of each inlet temperature. The optimization result could provide a guideline for the optimization of the CCR unit.

关 键 词：催化重整 集总 动力学模型 参数估计 

分 类 号：TQ018[化学工程]