Lumping gasoline with molecular properties and density peak clustering

doi:10.11949/0438-1157.20221231

Abstract

Abstract:

With advancement in petroleum molecular management technology, oil and its downstream products like gasoline are characterized by their real molecular composition. While the molecular characterization of oil products brings more detailed information, it also leads to a sharp increase in the scale and computational complexity of oil refining process simulation and optimization models. To solve this problem, a density peak clustering technique, which does not require a predetermined number of categories, is applied and the oil is lumped by thermodynamic properties of the real components, thus the oil product is fully characterized by a small number of pseudo-components. Taking a desulfurized gasoline feed in a certain process as an example, the density peak clustering lumping method was applied to the lumping of this gasoline. The results indicated that the number of components of gasoline products is greatly reduced by the proposed approach, and the simulation efficiency of the gasoline fractionator is effectively improved while the simulation accuracy is guaranteed.

Key words: petroleum molecular management, oil lumping, density peak clustering, algorithm, petroleum, thermodynamic properties

摘要：

随着石油分子管理技术的进步，石油及其下游的汽油等产品逐渐以其真实分子组成进行表征。而油品的分子表征在带来更详实信息的同时，也导致炼油过程模拟与优化模型的规模和计算量急剧上升。针对这一问题，应用无须事先确定类别数的密度峰聚类技术，并基于真实组分的热力学性质对油品进行集总，从而以少量虚拟组分对产品油进行充分表征。针对某装置的脱硫汽油集总结果表明，所提出的密度峰聚类集总方法极大地降低了表征汽油产品的分子数目，并在保证模拟精度的同时，有效地提高了汽油分馏塔的模拟计算效率。

关键词: 石油分子管理, 油品集总, 密度峰聚类, 算法, 石油, 热力学性质

CLC Number:

TE 624

Huaixu LI, Xiaoyan SUN, Shaohui TAO, Li XIA, Shuguang XIANG. Lumping gasoline with molecular properties and density peak clustering[J]. CIESC Journal, 2022, 73(12): 5449-5460.

李怀旭, 孙晓岩, 陶少辉, 夏力, 项曙光. 基于分子热力学性质和密度峰聚类的脱硫汽油集总[J]. 化工学报, 2022, 73(12): 5449-5460.

Figures/Tables 18

Fig.1 Standardized data distribution

Fig.2 Flash calculation results at 0.1 MPa for different lumping

Fig.3 Variation of MAE and computational complexity of the flash temperature with the increase of the number of lumps

Fig.4 Schematic diagram of the variation of calculation accuracy and component distribution with the number of lumps

Fig.5 Decision diagram of the number of lumps and center components

Table 1 Partial properties of outlier components

Molecular formula

Component

(28—162)

T_b /K

(169—517)

P_c /MPa

(2.11—5.69)

T_c/K

(282—772)

$ω$

(0.08—0.5)

C₁₂H₂₆

C₁₂H₈S

C₁₂H₁₈

1,4-diisopropylbenzene

Table 1 Partial properties of outlier components

Molecular formula

Component

(28—162)

T_b /K

(169—517)

P_c /MPa

(2.11—5.69)

T_c/K

(282—772)

$ω$

(0.08—0.5)

C₁₂H₂₆

dodecane

170.34

489.47

1.82

658.00

0.57

C₁₂H₈S

dibenzothiophene

184.26

604.61

3.86

897.00

0.40

C₁₂H₁₈

1,4-diisopropylbenzene

162.27

483.65

2.45

689.00

0.39

Fig.6 Schematic diagram of outlier detection

Fig.7 Lumped results of 287 components and visualization of results

Table 2 Partial properties of center components and corresponding pseudo-components

Center component/pseudo-components	MW	T_b/K	P_c/MPa	T_c/K	$ω$
cis-2-butene / PC1	56.11 / 57.90	276.87 / 275.98	4.21 / 4.24	435.50 / 440.22	0.20 / 0.16
cis-2-pentene / PC2	70.13 / 71.24	310.08 / 305.00	3.64 / 3.19	475.00 / 463.04	0.24 / 0.23
cis-3-hexene / PC3	84.16 / 87.78	339.60 / 347.87	3.17 / 3.27	509.00 / 526.40	0.28 / 0.25
3-methylheptane / PC4	114.23 / 114.17	392.08 / 391.78	2.55 / 2.68	563.60 / 571.37	0.37 / 0.33
2,2-dimethyloctane / PC5	142.28 /142.26	430.05 / 438.46	2.16 / 2.23	602.00 / 617.68	0.43 / 0.41
(1S,3S)-1,2,3-trimethylcyclopentane / PC6	112.22 / 117.14	390.35 / 418.68	2.90 / 3.16	579.82 / 630.26	0.28 / 0.30
1,4-dimethyl-2-ethylbenzene / PC7	134.22 / 134.15	459.98 / 467.90	2.88 / 3.38	663.00 / 700.67	0.41 / 0.31
2-hexene, 4-methyl-,(2Z)- / PC8	98.19 / 103.08	359.22 / 391.82	2.99 / 3.52	527.40 / 595.92	0.34 / 0.26
2,5-dimethylheptane / PC9	128.26 / 127.79	407.10 / 414.78	2.36 / 2.53	580.70 / 632.40	0.38 / 0.37
1-ethyl-2-propylbenzene / PC10	148.25 / 156.89	473.94 / 479.27	2.57 / 2.35	672.00 / 677.56	0.44 / 0.42

Table 2 Partial properties of center components and corresponding pseudo-components

Center component/pseudo-components	MW	T_b/K	P_c/MPa	T_c/K	$ω$
cis-2-butene / PC1	56.11 / 57.90	276.87 / 275.98	4.21 / 4.24	435.50 / 440.22	0.20 / 0.16
cis-2-pentene / PC2	70.13 / 71.24	310.08 / 305.00	3.64 / 3.19	475.00 / 463.04	0.24 / 0.23
cis-3-hexene / PC3	84.16 / 87.78	339.60 / 347.87	3.17 / 3.27	509.00 / 526.40	0.28 / 0.25
3-methylheptane / PC4	114.23 / 114.17	392.08 / 391.78	2.55 / 2.68	563.60 / 571.37	0.37 / 0.33
2,2-dimethyloctane / PC5	142.28 /142.26	430.05 / 438.46	2.16 / 2.23	602.00 / 617.68	0.43 / 0.41
(1S,3S)-1,2,3-trimethylcyclopentane / PC6	112.22 / 117.14	390.35 / 418.68	2.90 / 3.16	579.82 / 630.26	0.28 / 0.30
1,4-dimethyl-2-ethylbenzene / PC7	134.22 / 134.15	459.98 / 467.90	2.88 / 3.38	663.00 / 700.67	0.41 / 0.31
2-hexene, 4-methyl-,(2Z)- / PC8	98.19 / 103.08	359.22 / 391.82	2.99 / 3.52	527.40 / 595.92	0.34 / 0.26
2,5-dimethylheptane / PC9	128.26 / 127.79	407.10 / 414.78	2.36 / 2.53	580.70 / 632.40	0.38 / 0.37
1-ethyl-2-propylbenzene / PC10	148.25 / 156.89	473.94 / 479.27	2.57 / 2.35	672.00 / 677.56	0.44 / 0.42

Fig.8 Hydrocarbon composition and carbon number distribution of the subcomponents of pseudo-component

Fig.9 Comparison of flash temperature curves at 0.1 MPa for different lumped methods

Fig.10 Comparison of the absolute error of the flash heat duty (compared to the heat load of the real component) of different lumped methods

Fig.11 Comparison of the flash temperature of the center component and the pseudo-component at different vaporization fractions at 0.1 MPa

Fig.12 Comparison of the PT phase envelopes of center component and pseudo-component

Table 3 Boiling point gap between each pseudo-component and its center component

Pseudo-component	T_b (Pseudo-component)/K	T_b (Center component)/K	Absolute error/K
PC1	275.98	276.87	0.89
PC2	305.00	310.10	5.10
PC3	347.87	339.60	8.27
PC4	391.78	392.08	0.30
PC5	438.46	430.05	8.41
PC6	418.68	390.35	28.33
PC7	467.90	459.98	7.92
PC8	391.82	359.22	32.60
PC9	414.78	408.00	6.78
PC10	479.27	473.94	5.33

Fig.13 Display of local density and spatial location of each real component in PC8

Fig.14 Feeding conditions and distillation column parameter settings

Fig.15 Calculation results of distillation of different lumped results

References 33

1	Oliveira D F, Silva A C, Figueiredo W P, et al. Crude oil analysis by X-ray scattering technique[J]. X-Ray Spectrometry, 2019, 48(3): 195-201.
2	Abdulkadir I, Uba S, Salihu A A, et al. A rapid method of crude oil analysis using FT-IR spectroscopy[J]. Nigerian Journal of Basic and Applied Sciences, 2016, 24(1): 47.
3	田松柏, 龙军, 李长秀, 等. 石油轻馏分的分子水平表征技术研究进展[J]. 石油学报(石油加工), 2017, 33(4): 595-604.
	Tian S B, Long J, Li C X, et al. Research advance on analytical techniques of the petroleum light fractions at the molecular level[J]. Acta Petrolei Sinica (Petroleum Processing Section), 2017, 33(4): 595-604.
4	Shi Q, Zhang L Z, Xu C M, et al. Molecular characterization and modeling of petroleum refining process: frontiers and challenges[J]. Scientia Sinica Chimica, 2020, 50(2): 192-203.
5	Feng S, Cui C, Li K Y, et al. Molecular composition modelling of petroleum fractions based on a hybrid structural unit and bond-electron matrix (SU-BEM) framework[J]. Chemical Engineering Science, 2019, 201: 145-156.
6	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.
7	Ren Y, Liao Z W, Sun J Y, et al. Molecular reconstruction of naphtha via limited bulk properties: methods and comparisons[J]. Industrial & Engineering Chemistry Research, 2019, 58(40): 18742-18755.
8	于博, 周祥, 郭锦标. 石油组分分子水平划分方法研究进展[J]. 计算机与应用化学, 2014, 31(5): 583-586.
	Yu B, Zhou X, Guo J B. Research progress of classification of crude oil components on molecular level[J]. Computers and Applied Chemistry, 2014, 31(5): 583-586.
9	Weekman V W Jr. Model of catalytic cracking conversion in fixed, moving, and fluid-bed reactors[J]. Industrial & Engineering Chemistry Process Design and Development, 1968, 7(1): 90-95.
10	Montel F, Gouel P L. A new lumping scheme of analytical data for compositional studies[C]//SPE Annual Technical Conference and Exhibition. Houston, Texas: Society of Petroleum Engineers, 1984.
11	Leibovici C, Stenby E H, Knudsen K. A consistent procedure for pseudo-component delumping[J]. Fluid Phase Equilibria, 1996, 117(1/2): 225-232.
12	封松. 石油分子层次组成及分离过程的模型构建[D]. 北京: 中国石油大学(北京), 2019.
	Feng S. Molecular modeling of composition and separation process of petroleum fractions[D]. Beijing: China University of Petroleum, 2019.
13	张霖宙, 李凯宇, 史权, 等. 一种石油分子层次分离过程模拟的方法及其装置: 108279251A[P]. 2018-07-13.
	Zhang L Z, Li K Y, Shi Q, et al. Method and device for simulating petroleum molecular level separation process: 108279251A[P]. 2018-07-13.
14	Zheng L G. Improved K-means clustering algorithm based on dynamic clustering[J]. International Journal of Advanced Research in Big Data Management System, 2020, 4(1): 17-26.
15	张明微, 吴海涛. 一种优化初始聚类中心的k-means算法[J]. 上海师范大学学报(自然科学版), 2016, 45(5): 599-603.
	Zhang M W, Wu H T. A k-means algorithm to optimize the initial cluster centers[J]. Journal of Shanghai Normal University (Natural Sciences), 2016, 45(5): 599-603.
16	秦美华, 朱红求, 李勇刚, 等. 基于STA-K均值聚类的电化学废水处理过程离子浓度软测量[J]. 化工学报, 2019, 70(9): 3458-3464.
	Qin M H, Zhu H Q, Li Y G, et al. Soft-sensor method for ion concentration of electrochemical wastewater treatment based on STA-K-means clustering[J]. CIESC Journal, 2019, 70(9): 3458-3464.
17	Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.
18	Cheng Y Z. Mean shift, mode seeking, and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799.
19	Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition[J]. IEEE Transactions on Information Theory, 1975, 21(1): 32-40.
20	Mehmood R, Zhang G Z, Bie R F, et al. Clustering by fast search and find of density peaks via heat diffusion[J]. Neurocomputing, 2016, 208: 210-217.
21	Lyu B C, Wu W H, Hu Z Q. A novel bidirectional clustering algorithm based on local density[J]. Scientific Reports, 2021, 11: 14214.
22	陈叶旺, 申莲莲, 钟才明, 等. 密度峰值聚类算法综述[J]. 计算机研究与发展, 2020, 57(2): 378-394.
	Chen Y W, Shen L L, Zhong C M, et al. Survey on density peak clustering algorithm[J]. Journal of Computer Research and Development, 2020, 57(2): 378-394.
23	Flores K G, Garza S E. Density peaks clustering with gap-based automatic center detection[J]. Knowledge-Based Systems, 2020, 206: 106350.
24	Ding J J, He X X, Yuan J Q, et al. Automatic clustering based on density peak detection using generalized extreme value distribution[J]. Soft Computing, 2018, 22(9): 2777-2796.
25	毕荣山, 韩智慧, 陶少辉, 等. 基于热扩散核密度确定密度峰值法的历史工况识别[J]. 化工学报, 2022, 73(4): 1615-1622.
	Bi R S, Han Z H, Tao S H, et al. Recognizing historical operating conditions by determining the density peaks at kernel density estimation of heat diffusion[J]. CIESC Journal, 2022, 73(4): 1615-1622.
26	Rodriguez M Z, Comin C H, Casanova D, et al. Clustering algorithms: a comparative approach[J]. PLoS One, 2019, 14(1): e0210236.
27	Sun L, Liu R N, Xu J C, et al. An adaptive density peaks clustering method with fisher linear discriminant[J]. IEEE Access, 2019, 7: 72936-72955.
28	Sehgal G, Garg K. Comparison of various clustering algorithms[J]. International Journal of Computer Science and Information Technologies, 2014, 5(3): 3074-3076.
29	Leibovici C F. A consistent procedure for the estimation of properties associated to lumped systems[J]. Fluid Phase Equilibria, 1993, 87(2): 189-197.
30	张建忠, 张彪, 王仁安. 用改进的Riazi-Daubert法预测烃类临界性质[J]. 石油炼制与化工, 1998, 29(3): 55-56.
	Zhang J Z, Zhang B, Wang R A. Prediction of critical properties of hydrocarbons with modified riazi daubert method[J]. Petroleum Processing and Petrochemicals, 1998, 29(3): 55-56.
31	齐丽, 孙晓岩, 王建平, 等. 石油馏分偏心因子估算方法及评价[J]. 计算机与应用化学, 2017, 34(10): 774-779.
	Qi L, Sun X Y, Wang J P, et al. Evaluation of estimation method for acentric factor of petroleum fractions[J]. Computers and Applied Chemistry, 2017, 34(10): 774-779.
32	孙兰义. 化工过程模拟实训: Aspen Plus教程[M]. 2版. 北京: 化学工业出版社, 2017: 477.
	Sun L Y. Chemical Process Simulation Training: Aspen Plus Tutorial[M]. 2nd ed. Beijing: Chemical Industry Press, 2017: 477.
33	Lv Y, Liu M D, Xiang Y. Fast searching density peak clustering algorithm based on shared nearest neighbor and adaptive clustering center[J]. Symmetry, 2020, 12(12): 2014.

[1]	Yue CAO, Chong YU, Zhi LI, Minglei YANG. Industrial data driven transition state detection with multi-mode switching of a hydrocracking unit [J]. CIESC Journal, 2023, 74(9): 3841-3854.
[2]	Dian LIN, Guomei JIANG, Xiubin XU, Bo ZHAO, Dongmei LIU, Xu WU. Preparation and drag reduction effect of silicon-based liquid-like anti-crude-oil-adhesion coatings [J]. CIESC Journal, 2023, 74(8): 3438-3445.
[3]	Chengying ZHU, Zhenlei WANG. Operation optimization of ethylene cracking furnace based on improved deep reinforcement learning algorithm [J]. CIESC Journal, 2023, 74(8): 3429-3437.
[4]	Gang YIN, Yihui LI, Fei HE, Wenqi CAO, Min WANG, Feiya YAN, Yu XIANG, Jian LU, Bin LUO, Runting LU. Early warning method of aluminum reduction cell leakage accident based on KPCA and SVM [J]. CIESC Journal, 2023, 74(8): 3419-3428.
[5]	Xuejin GAO, Yuzhuo YAO, Huayun HAN, Yongsheng QI. Fault monitoring of fermentation process based on attention dynamic convolutional autoencoder [J]. CIESC Journal, 2023, 74(6): 2503-2521.
[6]	Xiaoyu YAO, Jun SHEN, Jian LI, Zhenxing LI, Huifang KANG, Bo TANG, Xueqiang DONG, Maoqiong GONG. Research progress in measurement methods in vapor-liquid critical properties of mixtures [J]. CIESC Journal, 2023, 74(5): 1847-1861.
[7]	Ke CHEN, Li DU, Ying ZENG, Siying REN, Xudong YU. Phase equilibria and calculation of quaternary system LiCl+MgCl₂+CaCl₂+H₂O at 323.2 K [J]. CIESC Journal, 2023, 74(5): 1896-1903.
[8]	Shumin ZHENG, Pengcheng GUO, Jianguo YAN, Shuai WANG, Wenbo LI, Qi ZHOU. Experimental and predictive study on pressure drop of subcooled flow boiling in a mini-channel [J]. CIESC Journal, 2023, 74(4): 1549-1560.
[9]	Sheng’an ZHANG, Guilian LIU. Multi-objective optimization of high-efficiency solar water electrolysis hydrogen production system and its performance [J]. CIESC Journal, 2023, 74(3): 1260-1274.
[10]	Xuerong GU, Shuoshi LIU, Siyu YANG. Research on multi-parameter optimization method based on parallel EGO and surrogate-assisted model [J]. CIESC Journal, 2023, 74(3): 1205-1215.
[11]	Yuanjing MAO, Zhi YANG, Songping MO, Hao GUO, Ying CHEN, Xianglong LUO, Jianyong CHEN, Yingzong LIANG. Estimation of SAFT-VR Mie equation of state parameters and thermodynamic properties of C₆—C₁₀ alcohols [J]. CIESC Journal, 2023, 74(3): 1033-1041.
[12]	Jiahui CHEN, Xinze YANG, Guzhong CHEN, Zhen SONG, Zhiwen QI. A critical discussion on developing molecular property prediction models: density of ionic liquids as example [J]. CIESC Journal, 2023, 74(2): 630-641.
[13]	Jiaqing ZHANG, Rongpei JIANG, Weikang SHI, Boxiang WU, Chao YANG, Zhaohui LIU. Study on viscosity-temperature characteristics and component characteristics of rocket kerosene [J]. CIESC Journal, 2023, 74(2): 653-665.
[14]	Wenting CHENG, Jie LI, Li XU, Fangqin CHENG, Guoji LIU. Experiment and prediction for the solubility of AlCl₃·6H₂O in FeCl₃, CaCl₂, KCl and KCl-FeCl₃solutions [J]. CIESC Journal, 2023, 74(2): 642-652.
[15]	Haiou YUAN, Fangjun YE, Shuo ZHANG, Yiqing LUO, Xigang YUAN. Synthesis of heat-integrated distillation sequences with intermediate heat exchangers [J]. CIESC Journal, 2023, 74(2): 796-806.