关键词: 催化重整, 反应动力学, 分子类型-同系物矩阵, 反应网络, 智慧工厂, 遗传算法, 计算机模拟

Abstract:

In the construction of smart factory in petrochemical enterprises, the establishment of molecular level device models is an important element in the transformation of enterprises to a refined and intelligent production model. In this paper, based on the naphtha composition and molecular type-homologue series (MTHS) matrix model, a deterministic molecular library of naphtha containing 270 molecules was established and a naphtha molecular reconstruction model was constructed, whose simulation values matched well with the actual values, achieving the goal of predicting the detailed composition of naphtha fractions by using macroscopic physical properties as input information. The reaction network simplification rules were established based on the reaction mechanism of catalytic reforming process, and rule input network generator (RING) was innovatively applied to a complex mixture such as naphtha to construct a catalytic reforming process reaction network containing 865 molecules and 6616 reactions. The genetic algorithm was used to estimate the reaction kinetic parameters and construct a kinetic model of catalytic reforming reaction at the molecular level, and the absolute error between its simulated and actual values for the product composition of the plant was 0.85%, and the product prediction at the molecular level can be realized. The molecular level model can be used to guide the operation of catalytic reforming plants and to build an intelligent model, the model construction method and ideas can be used to build molecular level models for petrochemical companies.

Key words: catalytic reforming, reaction kinetics, molecular type-homologue series matrix, reaction network, smart factory, genetic algorithm, computer simulation