基于结构导向集总的分子级催化重整过程建模方法

Molecular level modeling method of catalytic reforming based on structure-oriented lumping concept

本文提出了一种基于结构导向集总的催化重整过程建模方法,能够从分子水平描述催化重整过程原料分子到产物分子的转化过程,所述方法包括以下步骤:(1)采集催化重整过程原料和反应产物的样品,进行分子组成分析;(2)根据反应器及使用催化剂的特征建立反应规则集;(3)使用原料分子组成与反应规则自动生成反应网络;(4)估算反应过程的转化率;(5)调整反应深度,计算催化重整过程产物的分子组成;(6)对照计算的分子组成结果与实际分析的分子组成数据,计算模型预测误差;若实际分析分子组成数据与预测计算结果之间误差较大,重复步骤(4)～(6),直到最终的模型预测结果与实际分析检测数据误差满足精度要求。根据该方法建立分子级的催化重整过程模型,使用352种原料分子,17条反应规则,建立反应网络,包含中间产物、产品分子533种,共有分子反应转化路径617条。针对不同的工况,模型的预测结果的平均相对误差小于3%。根据所得到的模型,可以对催化重整过程进行更精细的优化,或者为炼油全流程计划优化模型提供预测数据。

Catalytic reforming is an important unit in oil refining. In this paper, a molecular level modeling method based on structure-oriented lumping concept is proposed to describe catalytic reforming process. The proposed method comprises of six steps: i) collecting and analyzing compositions of feedstock and product sample, ii) building reaction rules based on the feature of reactor and catalysts, iii) generating the reaction network by composition of feedstock and reaction rules, iv) evaluating the conversion rate for each reactions,v) adjusting the reaction depth to calculate product composition, vi) comparing the calculation result and the product composition, if the results don’t satisfy the requirements, then repeat steps iv to vi until the results are accurate enough. In the proposed catalytic reforming model, there are 352 molecular in feedstock, 17 reaction rules, 533 products molecular, and 617 conversion paths in the reaction network. Compare to different reaction situations, the prediction results errors are less than 3%. Base on the proposed model, the operation condition of reactor can be optimized, or the planning model of total site can be improved.