重油加氢过程集总动力学研究进展

doi:10.12422/j.issn.1006-396X.2023.02.002

石油化工高等学校学报 ›› 2023, Vol. 36 ›› Issue (2): 10-19.DOI: 10.12422/j.issn.1006-396X.2023.02.002

重油加氢过程集总动力学研究进展

杨依武¹(), 彭冲²(), 梁守才³, 侯政煜³, 谢飞¹

^1.辽宁石油化工大学石油天然气工程学院，辽宁抚顺 113001
^2.大连理工大学化工学院/精细化工国家重点实验室，辽宁大连 116024
^3.中国石油天然气集团有限公司辽河油田油气集输公司，辽宁盘锦 124010

收稿日期:2022-08-23 修回日期:2022-11-29 出版日期:2023-04-25 发布日期:2023-05-06
通讯作者: 彭冲
作者简介:杨依武（1998⁃），男，硕士研究生，从事油气储运工程的研究；E⁃mail：2370686588@qq.com。
基金资助:
国家自然科学基金项目(22122807);国家重点计划项目(2017YFB0306503)

Progress in Lumped Kinetic of Heavy Oil Hydrotreating

Yiwu Yang¹(), Chong Peng²(), Shoucai Liang³, Zhengyu Hou³, Fei Xie¹

^1.College of Petroleum Engineering，Liaoning Petrochemical University，Fushun Liaoning 113001，China
^2.School of Chemical Engineering/State Key Laboratory of Fine Chemicals，Dalian University of Technology，Dalian Liaoning 116024，China
^3.Liaohe Oilfield Oil and Gas Gathering and Transportation Company，China National Petroleum Corporation，Panjin Liaoning 124010，China

Received:2022-08-23 Revised:2022-11-29 Published:2023-04-25 Online:2023-05-06
Contact: Chong Peng

摘要/Abstract

摘要：

加氢裂化是炼油与石化行业的关键技术，借助反应动力学建模来深入理解加氢裂化反应机理具有重要的理论和现实意义。对重油馏分加氢动力学模型的研究进展进行了综述，系统介绍了建模方式对应模型参数的计算方法和对产物的预测方法；论述了反应动力学建模从初期简单地从宏观角度按照馏程划分的离散集总模型、根据生产方案划分的连续集总模型，发展到在复杂微观的分子水平上建立集总模型的过程；从预测产品成分的能力、参数估计的难度、速率系数和进料依赖性以及所需实验数据等方面，对不同模型的优缺点进行了比较；突出说明了集总模型的工业实用性和分子集总中分子层次反应网络构建及求解方法。根据建模难易程度、计算机运算能力及分析技术发展趋势，对集总动力学模型的发展前景进行了展望。

关键词: 加氢裂化, 集总, 动力学, 分子集总, 减压瓦斯油

Abstract:

Hydrocracking is a crucial technology in the refining and petrochemical sectors. It is of great theoretical and practical significance to deeply understand the mechanism of hydrocracking reactions with the aid of reaction kinetics modeling. This paper recounted the research progress in the kinetic modeling of heavy oil distillation and hydrotreating and shows the methods for computing model parameters corresponding to the modeling method and predicting products. The study showed that the reaction kinetics modeling evolved from the decentralized lumped model based on the distillation range from a macroscopic perspective to a continuous lumped model based on production scenarios and then to a complicated microscopic lumped model at the molecular level. In addition, the study compared the advantages and disadvantages of various models in terms of their capacity to reliably predict the composition of products, the intricacy of parameter estimation, rate coefficients, feed dependence, and experimental data required. It also explained the industrial applicability of the lumped model as well as the construction and method for solving molecular hierarchical reaction networks in molecular lumps. Finally, it gives the development prospects of the lumped kinetic model according to the modeling difficulties, the computing power of computers, and the analysis of technology development trends.

Key words: Hydrocracking, Lumped, Kinetics, Molecular lump, Vacuum gas oil

中图分类号:

TQ018

杨依武, 彭冲, 梁守才, 侯政煜, 谢飞. 重油加氢过程集总动力学研究进展[J]. 石油化工高等学校学报, 2023, 36(2): 10-19.

Yiwu Yang, Chong Peng, Shoucai Liang, Zhengyu Hou, Fei Xie. Progress in Lumped Kinetic of Heavy Oil Hydrotreating[J]. Journal of Petrochemical Universities, 2023, 36(2): 10-19.

图/表 8

图1 3类集总模型主要特点

Fig.1 Key features of the three?class lump model

图2 具有代表性的原油实沸点曲线

Fig.2 Representative true boiling point curves for crude oil

表1 M.A.Pacheco等基于B.E.Stangeland改进的加氢裂化动力学模型

Table 1 M.A.Pacheco improved hydrocracking kinetic model equations based on B.E.Stangeland

序号	动力学模型	备注
1	$d d t F i (t) = - k i F i (t) + ∑ j = 1 i - 1 p i j k i F i (t)$	Mass balance
2	$k (T) = k 0 [T + A (T 3 - T)]$	Cracking rate constant function
3	$P C i j = [y i j 2 + B (y i j 3 - y i j 2)] (1 - [C 4] j)$	Liquid product distribution function
4	$[C 4] j = C e x p [- 0.006 93 (1.8 T B P j - 229.5)]$	Weight fraction of butane
5	$y i j = T B P i - 2.5 (T B P i - 50) - 2.5$	TBP
6	$P i j = P C i j - P C i - 1, j$	Actual fraction of lighter component
7	$∑ i = 1 j - 2 P i, j M W i = (M W i - n j M W H 2)$	Mass balance for each individual reaction
8	$P C i j = [y i j 2 + B 1 y i j 3 - B 2 y i j 2] (1 - [C 4] j)$	Modified product distribution function( B₂)
9	$[C 4] j = C e x p [- w (1.8 T B P j - 229.5)]$	Modified weight fraction of butane (w)

表1 M.A.Pacheco等基于B.E.Stangeland改进的加氢裂化动力学模型

Table 1 M.A.Pacheco improved hydrocracking kinetic model equations based on B.E.Stangeland

序号	动力学模型	备注
1	$d d t F i (t) = - k i F i (t) + ∑ j = 1 i - 1 p i j k i F i (t)$	Mass balance
2	$k (T) = k 0 [T + A (T 3 - T)]$	Cracking rate constant function
3	$P C i j = [y i j 2 + B (y i j 3 - y i j 2)] (1 - [C 4] j)$	Liquid product distribution function
4	$[C 4] j = C e x p [- 0.006 93 (1.8 T B P j - 229.5)]$	Weight fraction of butane
5	$y i j = T B P i - 2.5 (T B P i - 50) - 2.5$	TBP
6	$P i j = P C i j - P C i - 1, j$	Actual fraction of lighter component
7	$∑ i = 1 j - 2 P i, j M W i = (M W i - n j M W H 2)$	Mass balance for each individual reaction
8	$P C i j = [y i j 2 + B 1 y i j 3 - B 2 y i j 2] (1 - [C 4] j)$	Modified product distribution function( B₂)
9	$[C 4] j = C e x p [- w (1.8 T B P j - 229.5)]$	Modified weight fraction of butane (w)

图3 从离散集总到连续集总的推导过程

Fig.3 Derivation process from discrete to continuous lumping

图4 P.J.Becker提出的五参数连续集总模型反应网络示意图

Fig.4 The diagram of five?component continuous set total model response network proposed by P.J.Becker

图5 L.P.de Oliveira提出的随机性分子重构模型算法

Fig.5 Stochastic molecular reconstruction algorithm proposed by L.P.de Oliveira

图6 D.Hudebine提出的SR?EM方法及Y.Ren提出的改进的SR?EM方法

Fig.6 SR?EM method proposed by D. Hudebine and an improved SR?EM method proposed by Y. Ren

图7 W.J.Song提出的SOM?CNN模型的建模流程

Fig.7 Modeling process of the SOM?CNN model proposed by W.J.Song

参考文献 48

1	Marafi A， Albazzaz H， Rana M S. Hydroprocessing of heavy residual oil： Opportunities and challenges［J］. Catalysis Today， 2019， 329： 125⁃134.
2	Sahu R， Song B J， Im J S， et al. A review of recent advances in catalytic hydrocracking of heavy residues［J］. Journal of Industrial and Engineering Chemistry， 2015， 27： 12⁃24.
3	吴青. 炼化企业数字化工厂建设及其关键技术研究［J］. 无机盐工业， 2018， 50（2）： 1⁃7.
	Wu Q. Research on construction of digital factory of refinery enterprise and its key technologies［J］. Inorganic Chemicals Industry， 2018， 50（2）： 1⁃7.
4	Basak K， Sau M， Manna U， et al. Industrial hydrocracker model based on novel continuum lumping approach for optimization in petroleum refinery［J］. Catalysis Today， 2004， 98（1⁃2）： 253⁃264.
5	Ancheyta J， Sánchez S， Rodríguez M A. Kinetic modeling of hydrocracking of heavy oil fractions： A review［J］. Catalysis Today， 2006， 109（1⁃4）： 76⁃92.
6	Balasubramanian P， Pushpavanam S. Model discrimination in hydrocracking of vacuum gas oil using discrete lumped kinetics［J］. Fuel， 2008， 87（8⁃9）： 1660⁃1672.
7	Iplik E， Aslanidou I， Kyprianidis K. Hydrocracking： A perspective towards digitalization［J］.Sustainability，2020，12（17）： 7058.
8	Stangeland B E. A kinetic model for the prediction of hydrocracker yields［J］. Industrial & Engineering Chemistry Process Design and Development， 1974，13（1）： 71⁃76.
9	Mohanty S， Saraf D N， Kunzru D. Modeling of a hydrocracking reactor［J］. Fuel Processing Technology， 1991， 29（1⁃2）： 1⁃17.
10	Pacheco M A， Dassori C G. Hydrocracking： An improved kinetic model and reactor modeling［J］. Chemical Engineering Communications， 2002， 189（12）： 1684⁃1704.
11	Palos R， Gutiérrez A， Hita I， et al. Kinetic modeling of hydrotreating for enhanced upgrading of light cycle oil［J］. Industrial & Engineering Chemistry Research， 2019， 58（29）： 13064⁃13075.
12	Olmos⁃Cerda E H， Laredo G C， Pérez⁃Romo P， et al. Selective hydrogenation of light cycle oil for BTX and gasoline production purposes［J］. International Journal of Chemical Reactor Engineering， 2022， 20（1）： 69⁃82.
13	Morales⁃Blancas M， Mederos⁃Nieto F S， Elizalde I， et al. Discrete lumping kinetic models for hydrodesulfuration and hydrocracking of a mixture of FCC feedstock and light gasoil［J］. Chemical Papers， 2022， 76（8）： 4885⁃4891.
14	Li M L， Ren T X， Sun Y D. Analysis of reaction path and different lumped kinetic models for asphaltene hydrocracking［J］. Fuel， 2022， 325： 124840.
15	Soto⁃Azuara L A， Ramírez⁃López R， del Carmen Monterrubio⁃Badillo M， et al. Mathematical modeling of the hydrocracking kinetics of a heavy oil fraction using the discrete lumping approach： The effect of the variation of the lump number［J］. Reaction Kinetics， Mechanisms and Catalysis， 2022， 135（2）： 655⁃667.
16	Shi S， Tan W， Sun J S. Progress in kinetic predictions for complex reaction of hydrocarbons： From mechanism studies to industrial applications［J］. Reviews in Chemical Engineering， 2016， 32（3）： 363⁃378.
17	Laxminarasimhan C S， Verma R P， Ramachandran P A. Continuous lumping model for simulation of hydrocracking［J］. AIChE Journal， 1996， 42（9）： 2645⁃2653.
18	Chou M Y， Ho T C. Continuum theory for lumping nonlinear reactions［J］. AIChE Journal， 1988， 34（9）： 1519⁃1527.
19	Elizalde I， Ancheyta J. Modeling the simultaneous hydrodesulfurization and hydrocracking of heavy residue oil by using the continuous kinetic lumping approach［J］. Energy & Fuels， 2012， 26（4）： 1999⁃2004.
20	Becker P J， Serrand N， Celse B， et al. Comparing hydrocracking models： Continuous lumping vs. single events［J］. Fuel， 2016， 165： 306⁃315.
21	Naderi H， Shokri S， Ahmadpanah S J. Optimization of kinetic lumping model parameters to improve products quality in the hydrocracking process［J］. Brazilian Journal of Chemical Engineering， 2018， 35： 757⁃768.
22	Faraji D， Zabihi S， Ghadiri M， et al. Computational fluid dynamic modeling and simulation of hydrocracking of vacuum gas oil in a fixed⁃bed reactor［J］. ACS Omega， 2020， 5（27）： 16595⁃16601.
23	Aris R， Gavalas G R. On the theory of reactions in continuous mixtures［J］. Philosophical Transactions of the Royal Society A： Mathematical，Physical and Engineering Sciences， 1966， 260（1112）： 351⁃393.
24	Lababidi H M S， AlHumaidan F S. Modeling the hydrocracking kinetics of atmospheric residue in hydrotreating processes by the continuous lumping approach［J］. Energy & Fuels， 2011， 25（5）： 1939⁃1949.
25	Chen Z Y， Feng S， Zhang L Z， et al. Molecular⁃level kinetic modelling of fluid catalytic cracking slurry oil hydrotreating［J］. Chemical Engineering Science， 2019， 195： 619⁃630.
26	Djokic M R， Muller H， Ristic N D， et al. Combined characterization using HT⁃GC× GC⁃FID and FT⁃ICR MS： A pyrolysis fuel oil case study［J］. Fuel Processing Technology， 2018， 182： 15⁃25.
27	He G， Zhou C L， Luo T， et al. Online optimization of fluid catalytic cracking process via a hybrid model based on simplified structure⁃oriented lumping and case⁃based reasoning［J］. Industrial & Engineering Chemistry Research， 2021， 60（1）： 412⁃424.
28	Hudebine D， Verstraete J J. Molecular reconstruction of LCO gasoils from overall petroleum analyses［J］. Chemical Engineering Science， 2004， 59（22⁃23）： 4755⁃4763.
29	Liguras D K， Allen D T. Structural models for catalytic cracking.1.Model compound reactions［J］. Industrial & Engineering Chemistry Research， 1989， 28（6）： 665⁃673.
30	Hudebine D， Verstraete J J. Reconstruction of petroleum feedstocks by entropy maximization. Application to FCC gasolines［J］. Oil & Gas Science and Technology⁃Revue d’IFP Energies Nouvelles， 2011， 66（3）： 437⁃460.
31	Mei H， Cheng H， Wang Z L， et al. Molecular characterization of petroleum fractions using state space representation and its application for predicting naphtha pyrolysis product distributions［J］. Chemical Engineering Science， 2017， 164： 81⁃89.
32	Cui C， Billa T， Zhang L Z， et al. Molecular representation of the petroleum gasoline fraction［J］. Energy & Fuels， 2018， 32（2）： 1525⁃1533.
33	Bojkovic A， Dijkmans T， Dao Thi H， et al. Molecular reconstruction of hydrocarbons and sulfur⁃containing compounds in atmospheric and vacuum gas oils［J］. Energy & Fuels， 2021， 35（7）： 5777⁃5788.
34	Chen Z Y， Guan D， Zhang X J， et al. A mass⁃temperature decoupled discretization strategy for large⁃scale molecular⁃level kinetic model［J］. Chemical Engineering Science， 2022， 249： 117348.
35	Pernalete C G， Ibanez J， Mendes P S F， et al. Hydrocracking of complex mixtures： From bulk properties， over fundamental kinetics to detailed product composition［J］. Catalysis Today， 2021， 378： 189⁃201.
36	Guan D， Chen Z Y， Chen X， et al. Molecular⁃level heavy petroleum hydrotreating modeling and comparison with high⁃resolution mass spectrometry［J］. Fuel， 2021， 297： 120792.
37	de Oliveira L P， Verstraete J J， Kolb M. Molecule⁃based kinetic modeling by Monte Carlo methods for heavy petroleum conversion［J］. Science China Chemistry， 2013， 56： 1608⁃1622.
38	Neurock M， Libanati C， Nigam A， et al. Monte Carlo simulation of complex reaction systems： Molecular structure and reactivity in modelling heavy oils［J］. Chemical Engineering Science， 1990， 45（8）： 2083⁃2088.
39	Hudebine D， Verstraete J， Chapus T. Statistical reconstruction of gas oil cuts［J］. Oil & Gas Science and Technology⁃Revue d’IFP Energies Nouvelles， 2011， 66（3）： 461⁃477.
40	Ren Y，Liao Z W， Sun J Y， et al. Molecular reconstruction：Recent progress toward composition modeling of petroleum fractions［J］.Chemical Engineering Journal， 2019， 357： 761⁃775.
41	Guan D， Zhang L Z. Initial guess estimation and fast solving of petroleum complex molecular reconstruction model［J］. AIChE Journal， 2022， 68（10）： 17782.
42	Zhao G Y， Yang M L， Du W Y， et al. A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha［J］. Chinese Journal of Chemical Engineering， 2022， 51： 153⁃167.
43	Quann R J， Jaffe S B. Structure⁃oriented lumping： Describing the chemistry of complex hydrocarbon mixtures［J］. Industrial & Engineering Chemistry Research， 1992， 31（11）： 2483⁃2497.
44	Jaffe S B， Freund H， Olmstead W N. Extension of structure⁃oriented lumping to vacuum residua［J］.Industrial & Engineering Chemistry Research， 2005， 44（26）： 9840⁃9852.
45	Chen J C， Fang Z， Qiu T. Molecular reconstruction model based on structure oriented lumping and group contribution methods［J］. Chinese Journal of Chemical Engineering， 2018， 26（8）： 1677⁃1683.
46	Guan Y M， Guan D， Zhang C， et al. Diesel molecular composition and blending modeling based on SU⁃BEM framework［J］. Petroleum Science， 2022， 19（2）： 839⁃847.
47	Song W J， Mahalec V， Long J， et al. Modeling the hydrocracking process with deep neural networks［J］. Industrial & Engineering Chemistry Research， 2020， 59（7）： 3077⁃3090.
48	Yu W X， Ye L， Qin X L， et al. Reaction behaviors of polycyclic aromatic hydrocarbon molecules in a diesel hydro⁃upgrading process based on the molecular⁃level reaction kinetic model［J］. Industrial & Engineering Chemistry Research， 2022， 61（17）： 5723⁃5733.

[1]	罗凤, 张万年, 王锐, 王凯, 戴淑敏, 何宇鹏, 秦玉才. 琥珀酸衍生物类非离子型表面活性剂的合成及性能评价[J]. 石油化工高等学校学报, 2024, 37(3): 11-16.
[2]	刘娜, 梁光敏, 季久玉, 周坤, 罗根祥, 李微. Salen金属配合物水解动力学拆分环氧氯丙烷研究进展[J]. 石油化工高等学校学报, 2023, 36(4): 53-62.
[3]	铁云艳, 汪杰, 江厚顺, 范龙飞, 敢家悦, 郭盼阳. 抑制剂对甲烷水合物生成规律影响的研究[J]. 石油化工高等学校学报, 2023, 36(3): 37-43.
[4]	高飞, 辛建旭, 郭志强, 李欣欣, 孙广宇. 含杂质CO₂物性变化规律及其机理研究[J]. 石油化工高等学校学报, 2022, 35(4): 18-25.
[5]	马钰，王璐璐，王吉林. 基于金属有机框架构筑的阴离子交换膜溶胀性能研究[J]. 石油化工高等学校学报, 2021, 34(3): 24-30.
[6]	郑健，李强，秦玉才，宋丽娟. Y分子筛中硅铝桥羟基结构调变规律的理论研究[J]. 石油化工高等学校学报, 2019, 32(1): 6-12.
[7]	王平平，刘丹，杨丽霞，张鹏，刘成伟. 纳米正硅酸锂的合成、表征及CO2吸附性能研究[J]. 石油化工高等学校学报, 2018, 31(5): 11-16.
[8]	李传宪，王璐，姚博，郭策，程浩，杨飞，孙广宇. POA分子在纳米SiO₂颗粒表面吸附的分子动力学模拟[J]. 石油化工高等学校学报, 2018, 31(5): 84-92.
[9]	吴方棣, 杨自涛, 范荣玉. 柑青醛合成新铃兰醛反应动力学研究[J]. 石油化工高等学校学报, 2018, 31(01): 17-22.
[10]	杨玲,张乐,宋丽娟. 稀土离子浓度对Y型分子筛传质动力学的影响[J]. 石油化工高等学校学报, 2017, 30(6): 12-16.
[11]	宋官龙,赵德智,吴青. 反应条件对渣油悬浮床加氢裂化反应的影响[J]. 石油化工高等学校学报, 2017, 30(5): 17-21.
[12]	于焕良,崔蕊,赵耀. 加氢裂化换热器结垢腐蚀原因分析[J]. 石油化工高等学校学报, 2017, 30(3): 15-19.
[13]	赫列布尼科夫V.N.,安东诺夫S.V.,哈米杜琳娜I.V.,梁萌,维诺库罗夫V.A.,谢苗诺夫A.P.,古欣P.A.. 高压注空气采油工艺的室内研究[J]. 石油化工高等学校学报, 2016, 29(5): 26-37.
[14]	宁建梅, 彭坤, 杨丽霞, 李艺, 桂建舟, 刘道胜, 吕振波. 杂多酸型离子液体的合成及在清洁氧化中的应用[J]. 石油化工高等学校学报, 2015, 28(6): 14-19.
[15]	王艳丹, 刘凯凯, 陈俊, 郑德, 郭绍辉. 新型席夫碱类橡胶防老剂的合成与性能评价[J]. 石油化工高等学校学报, 2015, 28(5): 8-12.

重油加氢过程集总动力学研究进展

Progress in Lumped Kinetic of Heavy Oil Hydrotreating

HTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 48

相关文章 15

编辑推荐

Metrics