Progress in Lumped Kinetic of Heavy Oil Hydrotreating

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

Abstract

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

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

CLC Number:

TQ018

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.

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.

Figures/Tables 8

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

Fig.2 Representative true boiling point curves for crude oil

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)

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)

Fig.3 Derivation process from discrete to continuous lumping

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

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

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

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

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