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乙烯工业与国民经济和人民生活息息相关,针对石脑油裂解制乙烯过程的模拟与优化成为研究热点。为了实现基于自由基的裂解动力学模型的石脑油裂解模拟,需要准确的石脑油详细烃组成数据。本文以石脑油详细烃组成预测系统研究为题,开展基于石脑油常规物性数据预测石脑油详细烃组成的研究工作,所完成的工作具有重要理论意义和应用价值。通过对不同原料产地的8套石脑油详细烃数据的分析,对物性相近的组分进行集总,最终确定了50种预测烃类。改进了利用PIONA值对组成进行族校正和碳数校正的方法。通过对6种不同地区石脑油组成的进一步研究,发现部分同分异构的组成所占比重具有一定规律,优化计算得到组成校正中部分同分异构组成间的分配权重,使组成预测精度进一步提高。
Ethylene industry and the national economy and people’s lives are closely related to the simulation and optimization of the ethylene cracking process naphtha cracking has become a research hot spot. In order to achieve a naphtha cracking simulation based on a radical-based cracking kinetic model, accurate naphtha-specific hydrocarbon composition data are required. In this paper, the detailed hydrocarbon composition prediction system of naphtha is taken as a subject to carry out the research on the prediction of naphtha specific hydrocarbon composition based on the conventional naphtha physical properties data. The work done has important theoretical significance and application value. Through the analysis of 8 sets of naphtha detailed hydrocarbon data of different raw materials origin, the components with similar physical properties are aggregated, and finally 50 kinds of predicted hydrocarbons are confirmed. Improved PIONA-based method for family calibration and carbon number calibration. Through the further study on the naphtha composition in six different regions, we find that the proportion of partial isomeric composition has a certain regularity. The distribution weight of partial isomeric composition in the composition correction is optimized and calculated, Further improve.