Designing novel alkoxypropylamine solvents for removing mercaptans from high-acidity natural gas

doi:10.16085/j.issn.1000-6613.2023-2244

Abstract

Abstract:

Mercaptans are hazardous components commonly found in natural gas. Compared to hydrogen sulfide (H₂S) and carbon dioxide (CO₂), mercaptans exhibit lower removal efficiency in the conventional amine-based absorption processes because of their lower acidity and reactivity. Herein, the solubility prediction methods based on COSMO-RS theory were employed to identify a kind of alternative solvents with promising dissolving performance. The removal efficiency of these solvents for methyl mercaptan was evaluated on a custom-made absorption apparatus using simulated high-acid natural gas as the feedstock. Furthermore, we employed quantum chemistry calculation to elucidate the mechanisms for dissolution of methyl mercaptan into these solvents. Present study demonstrates that the alkoxypropylamine solvents have effective solubility for methyl mercaptan, and the contents of hydrogen sulfide and organic sulfide can be reduced from 5% and 933mg S/m³(standard condition) in the raw gas to 0 and less than 20mg S/m³(standard condition) in the purified gas, respectively, by using alkoxypropylamine solutions with a concentration of no less than 20%(mass fraction) under the gas-to-liquid ratio of less than 250. The sulfur content of purified gas can meet the specification for Chinese first-grade commercial 《Natural Gas》 (GB17820—2018). Analyses on intermolecular interactions between the solvent and solute indicate that methyl mercaptan molecules can be captured in alkoxypropylamines via strong hydrogen bonding and big-area van der Waals interaction.

Key words: natural gas purification, methyl mercaptan, desulfurization solvent design, alkoxypropylamine, intermolecular interactions

摘要：

硫醇类化合物是酸性天然气中普遍存在的有害组分，相较于硫化氢和二氧化碳，硫醇类化合物的酸性和反应活性较低，难以通过传统的胺洗脱硫工艺实现深度脱除。本研究采用基于COSMO-RS理论的溶解度预测方法筛选了对甲硫醇具有理想溶解性能的溶剂，然后在小试吸收实验装置上评价了其对甲硫醇的脱除效率，进一步借助量化计算揭示了其高效吸收溶解甲硫醇的机制。结果表明，烷氧基丙胺化合物对甲硫醇具有较好的溶解性能；在气液比不高于250的吸收条件下，使用质量分数不低于20%的烷氧基丙胺溶液可将原料气中5%的硫化氢和933mg S/m³（标准状况）的有机硫分别脱除至0和20mg S/m³（标准状况）以下，净化气硫含量满足《天然气》（GB17820—2018）一类气指标要求；溶剂-溶质间弱相互作用分析表明，烷氧基丙胺通过较强的氢键作用和大面积范德华作用可高效捕获甲硫醇分子。

关键词: 天然气净化, 甲硫醇, 脱硫溶剂设计, 烷氧基丙胺, 相互作用

CLC Number:

TQ028.2⁺5

LIU Chuanlei, CHEN Yuxiang, GUO Guanchu, ZHAO Qiyue, JIANG Hao, SUN Hui, SHEN Benxian. Designing novel alkoxypropylamine solvents for removing mercaptans from high-acidity natural gas[J]. Chemical Industry and Engineering Progress, 2025, 44(1): 184-191.

刘传磊, 陈宇翔, 郭冠初, 赵起越, 姜豪, 孙辉, 沈本贤. 烷氧基丙胺类新型溶剂分子设计及其脱除高酸性天然气中硫醇[J]. 化工进展, 2025, 44(1): 184-191.

Figures/Tables 13

层级	物性	筛选标准
第一层	化学及结构稳定性	可以在酸碱性环境及吸收-解吸循环过程中稳定存在的结构
第二层	熔点	≤40℃（工况温度下为液态）
	沸点	≥160℃（解吸再生温度下为液态）
	水溶性	易溶于水
第三层	价格	有可查CAS号，可以购得或易于合成，使用成本保证收益
第三层	毒性&腐蚀性	低毒，对设备腐蚀弱

名称	CAS号	简写	纯度	采购来源
乙氧基丙胺	6291-85-6	3-EOPA	AR	上海麦克林生化科技股份有限公司
异丙氧基丙胺	2906-12-9	IPOPA	AR	上海麦克林生化科技股份有限公司
丁氧基丙胺	16499-88-0	3-BOPA	AR	上海麦克林生化科技股份有限公司
乙醇胺	141-43-5	MEA	AR	上海泰坦科技股份有限公司
二乙醇胺	111-42-2	DEA	AR	上海泰坦科技股份有限公司
二甘醇胺	929-06-6	DGA	AR	上海泰坦科技股份有限公司
N-甲基二乙醇胺	105-59-9	MDEA	AR	上海泰坦科技股份有限公司
碳酸丙烯酯	108-32-7	PC	AR	上海新铂化学技术有限公司
环丁砜	126-33-0	SUL	AR	上海新铂化学技术有限公司
模拟高酸性天然气	—	—	H₂S 5%, CO₂ 5% COS 311mg S/m³ 甲硫醇 622mg S/m³余量氮气	上海伟创标准气体有限公司
溶解度测定用混合气	—	—	甲硫醇 2700mg S/m³余量氮气	上海伟创标准气体有限公司

参数	核区域	化学键区域	弱相互作用区域	分子边界区域
$\|\nabla ρ (r)\|$	大	0~小	0~小	极小~小
$\sqrt[3]{ρ {(r)}^{4}}$	大	中	小	0~小
RDG	中	0~小	0~中	中~极大

参数	核区域	化学键区域	弱相互作用区域	分子边界区域
$\|\nabla ρ (r)\|$	大	0~小	0~小	极小~小
$\sqrt[3]{ρ {(r)}^{4}}$	大	中	小	0~小
RDG	中	0~小	0~中	中~极大

溶剂	H_K/kPa
3-EOPA	156.09
IPOPA	82.14
3-BOPA	149.70
MEA	214.36
DEA	15135.4
DGA	400.16
MDEA	553.88
PC	591.33
SUL	591.33

分子	编号	拓扑路径	ρ(r) (a.u.)	∇ρ(r)	键能 /kJ·mol^-1
乙氧基丙胺	33	C—H-----S—C	6.03×10^-3	1.88×10^-2	-2.25
	34	C—H-----H—C	5.74×10^-3	1.95×10^-2	-2.52
	37	C—H-----H—C	4.82×10^-3	1.59×10^-2	-1.27
	41	C—H-----S—C	4.69×10^-3	1.58×10^-2	-1.39
	44	C—H-----H—C	6.14×10^-3	2.16×10^-2	-2.63
	48	S—H-----O—C2	1.57×10^-2	5.50×10^-2	-11.50
异丙氧基丙胺	46	C—H-----H—C	6.06×10^-3	2.04×10^-2	-2.55
	51	C—H-----H—C	5.44×10^-3	2.01×10^-2	-1.97
	57	C—H-----H—C	6.02×10^-3	2.00×10^-2	-2.51
	60	S—H-----O—C2	1.52×10^-2	5.20×10^-2	-11.03
	62	C—H-----H—C	6.43×10^-3	2.07×10^-2	-2.90
	65	C—H------S—C	6.34×10^-3	2.08×10^-2	-2.81
	67	C—H------S—C	5.93×10^-3	1.96×10^-2	-2.43
丁氧基丙胺	53	C—H-----C—O	6.01×10^-3	2.25×10^-2	-2.50
	61	C—H-----S—C	5.63×10^-3	1.83×10^-2	-2.15
	66	S—H-----O—C2	1.57×10^-2	5.54×10^-2	-11.53
	67	C—H-----H—C	5.47×10^-3	1.76×10^-2	-2.00
	71	C—H-----H—C	5.68×10^-3	1.81×10^-2	-2.20
	74	C—H-----S—C	4.60×10^-3	1.55×10^-2	-1.19

溶剂	E_int/kJ·mol^-1	CED/J·cm^-3
3-EOPA	-23.82	288.02
3-EOPA	-23.66	256.37
3-BOPA	-23.18	247.11
MEA	-22.00	715.78
DEA	-17.86	571.95
DGA	-16.47	496.37
MDEA	-15.01	474.66
PC	-27.77	519.81
SUL	-26.53	502.41

References 13

14	CHEN Yuxiang, LIU Chuanlei, GUO Guanchu, et al. Machine-learning-guided reaction kinetics prediction towards solvent identification for chemical absorption of carbonyl sulfide[J]. Chemical Engineering Journal, 2022, 444: 136662.
15	CHEN Yuxiang, LIU Chuanlei, AN Yang, et al. Intelligent molecular identification approach to high-efficiency solvents for organosulfide capture using the active machine learning framework[J]. Energy & Fuels, 2023, 37(16): 12123-12135.
16	KLAMT A. Estimation of gas-phase hydroxyl radical rate constants of organic compounds from molecular orbital calculations[J]. Chemosphere, 1993, 26(7): 1273-1289.
17	KLAMT A, ECKERT F, HORNIG M. COSMO-RS: A novel view to physiological solvation and partition questions[J]. Journal of Computer-Aided Molecular Design, 2001, 15(4): 355-365.
18	BLUM Lorenz C, REYMOND Jean-Louis. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13[J]. Journal of the American Chemical Society, 2009, 131(25): 8732-8733.
19	LIU Chuanlei, CHEN Yuxiang, JIANG Hao, et al. Revealing the structure-interaction-dissolubility relationships through computational investigation coupled with solubility measurement: Toward solvent design for organosulfide capture[J]. Industrial & Engineering Chemistry Research, 2022, 61(20): 7183-7192.
20	杨世伦, 安阳, 孙辉, 等. 提高UDS溶剂脱除塔河油田伴生天然气中有机硫效率[J]. 化工进展, 2019, 38(10): 4534-4541.
	YANG Shilun, AN Yang, SUN Hui, et al. Modification on UDS solvent for enhanced organosulfides removal from Tahe associated natural gas[J]. Chemical Industry and Engineering Progress, 2019, 38(10): 4534-4541.
21	JOHNSON Erin R, KEINAN Shahar, Paula MORI-SÁNCHEZ, et al. Revealing noncovalent interactions[J]. Journal of the American Chemical Society, 2010, 132(18): 6498-6506.
22	BADER Richard F W. A quantum theory of molecular structure and its applications[J]. Chemical Reviews, 1991, 91(5): 893-928.
23	LI Chunyu, STRACHAN Alejandro. Cohesive energy density and solubility parameter evolution during the curing of thermoset[J]. Polymer, 2018, 135: 162-170.
24	OVERGAARD Jacob, IVERSEN Bo B. Charge density methods in hydrogen bond studies[M]//Structure and bonding. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010: 53-74.
1	PULHAN Afet, YORUCU Vedat, SINAN EVCAN Nusret. Global energy market dynamics and natural gas development in the Eastern Mediterranean region[J]. Utilities Policy, 2020, 64: 101040.
2	GUIDOTTI Tee L. Hydrogen sulfide: Advances in understanding human toxicity[J]. International Journal of Toxicology, 2010, 29(6): 569-581.
3	SULEMAN Humbul, MAULUD Abdulhalim Shah, MAN Zakaria. Review and selection criteria of classical thermodynamic models for acid gas absorption in aqueous alkanolamines[J]. Reviews in Chemical Engineering, 2015, 31(6): 599-639.
4	SAHA Biswajit, VEDACHALAM Sundaramurthy, DALAI Ajay K. Review on recent advances in adsorptive desulfurization[J]. Fuel Processing Technology, 2021, 214: 106685.
5	GHOSAL Kanchan, FREEMAN Benny D. Gas separation using polymer membranes: An overview[J]. Polymers for Advanced Technologies, 1994, 5(11): 673-697.
6	CHEN Chuan, XU Xijun, XIE Peng, et al. Pyrosequencing reveals microbial community dynamics in integrated simultaneous desulfurization and denitrification process at different influent nitrate concentrations[J]. Chemosphere, 2017, 171: 294-301.
7	SHAH Mansi S, TSAPATSIS Michael, Ilja SIEPMANN J. Hydrogen sulfide capture: From absorption in polar liquids to oxide, zeolite, and metal-organic framework adsorbents and membranes[J]. Chemical Reviews, 2017, 117(14): 9755-9803.
8	RAHMAN Mahmud A, MADDOX Robert N, MAINS G J. Reactions of carbonyl sulfide and methyl mercaptan with ethanolamines[J]. Industrial & Engineering Chemistry Research, 1989, 28(4): 470-475.
9	BURR Barry, LYDDON Lili. A comparison of physical solvents for acid gas removal[J]. GPA Annual Convention Proceedings, 2008, 1: 100-113.
10	AWAN Javeed A, TSIVINTZELIS Ioannis, VALTZ Alain, et al. Vapor-liquid-liquid equilibrium measurements and modeling of the methanethiol + methane + water ternary system at 304, 334, and 364 K[J]. Industrial & Engineering Chemistry Research, 2012, 51(35): 11561-11564.
11	于惠波, 沈本贤, 孙辉, 等. 改性UDS溶剂提高天然气中甲硫醇脱除效率的研究[J]. 石油炼制与化工, 2017, 48(7): 28-33.
	YU Huibo, SHEN Benxian, SUN Hui, et al. Study of improving MeSH removal efficiency by modified uds solvent from natural gas[J]. Petroleum Processing and Petrochemicals, 2017, 48(7): 28-33.
25	MOHAN Neetha, SURESH Cherumuttathu H. A molecular electrostatic potential analysis of hydrogen, halogen, and dihydrogen bonds[J]. The Journal of Physical Chemistry A, 2014, 118(9): 1697-1705.
26	EMAMIAN Saeedreza, LU Tian, KRUSE Holger, et al. Exploring nature and predicting strength of hydrogen bonds: A correlation analysis between atoms-in-molecules descriptors, binding energies, and energy components of symmetry-adapted perturbation theory[J]. Journal of Computational Chemistry, 2019, 40(32): 2868-2881.
12	Patrick WALTERS W, BARZILAY Regina. Applications of deep learning in molecule generation and molecular property prediction[J]. Accounts of Chemical Research, 2021, 54(2): 263-270.
13	SEIERSTAD Mark, TICHENOR Mark S, DESJARLAIS Renee L, et al. Novel reagent space: Identifying unorderable but readily synthesizable building blocks[J]. ACS Medicinal Chemistry Letters, 2021, 12(11): 1853-1860.

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