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| 用延迟焦化逐步回归法模型预测焦化产物的分布 | |
| 引用本文： | 梁朝林,沈本贤,刘纪昌,陈晓龙.用延迟焦化逐步回归法模型预测焦化产物的分布[J].华东理工大学学报(自然科学版),2009,35(2). |
| 作者姓名： | 梁朝林 沈本贤 刘纪昌 陈晓龙 |
| 作者单位： | 1. 华东理工大学化学工程联合国家重点实验室,上海,200237;茂名学院,化工与环境工程学院,广东,茂名,525000 2. 华东理工大学化学工程联合国家重点实验室,上海,200237 3. 茂名学院,电子信息学院,广东,茂名,525000 |
| 摘 要： | 因原油产地、加工方法等不同,减压渣油延迟焦化反应产物的分布及组成差异较大。对17种减压渣油的物理化学性质,如密度、残炭、灰分、金属含量、组成(饱和分、芳香分、胶质、沥青质)等进行了全面分析,并在管式焦化反应装置上分别进行焦化反应实验。采用逐步回归分析法对11组数据进行分析,首先确定对焦化反应产物分布的各项指标(包括焦炭收率、≤200℃轻油收率、>200℃重油收率、气体收率、气体中氢气浓度及甲烷浓度)影响显著的减压渣油理化性质,然后用多元回归拟合得到各理化性质的回归系数,建立延迟焦化装置产品分布预测模型,并根据6种减压渣油的性质对焦化产物分布进行预测,结果表明该预测模型的相对误差均小于5%。与已有模型相比,该模型不但可以同时预测焦化气体、轻油、重油和焦炭产率,还可以预测焦化气体中氢气和甲烷含量,从而为预测渣油的焦化产物分布、优化调配焦化原料的合理加工提供较为全面的参考信息。 |
| 关 键 词： | 减压渣油 生焦率 理化性质 逐步回归法 |
Prediction for Product Distribution of Delayed-Coking Process by Stepwise Regression Model |
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| LIANG Chao-lin,SHEN Ben-xian,LIU Ji-chang,CHEN Xiao-long.Prediction for Product Distribution of Delayed-Coking Process by Stepwise Regression Model[J].Journal of East China University of Science and Technology,2009,35(2). | |
| Authors: | LIANG Chao-lin SHEN Ben-xian LIU Ji-chang CHEN Xiao-long |
| Institution: | 1;2a;2b;1.State Key Laboratory of Chemical Engineering;East China University of Science and Technology;Shanghai 200237;China;2a.School of Chemical Engineering and Environmental Engineering;2b.School of Computer and Electronic Information;Maoming University;Maoming 525000;Guangdong;China |
| Abstract: | The coking product distribution and composition of the various vacuum residues are very(different) because these vacuum residues come from various oil fields and refined ways.Properties of 17(vacuum) residues,such as density,residue carbon(electric furnace method),ash,metallic content,SARA(saturate,aromatics,resins,asphaltene) fractions,are analyzed.Coking reactive tests of the vacuum residues are respectively appraised by using the tubular coking reactor.According to the stepwise regression (analysis) on 1... |
| Keywords: | vacuum residue coking yield physical and chemical properties stepwise regression |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |