T型微反应器中合成脂肪酸甲酯磺酸盐动力学及工艺优化

doi:10.16085/j.issn.1000-6613.2023-2127

摘要/Abstract

摘要：

降膜反应器合成脂肪酸甲酯磺酸盐存在内部温度分布不均、产率下降的问题。基于微反应器高效反应特性，本文尝试在T型微反应器中合成脂肪酸甲酯磺酸盐。考察了磺化反应过程中温度、摩尔比、老化时间、老化温度等因素对产物中活性物含量（产物中活性物的质量分数）的影响，采用响应面法探究各因素对活性物含量影响强弱顺序及交互作用，并在此基础上进行工艺条件优化。分析得出，各因素对活性物含量影响由强到弱的顺序为：三氧化硫与硬脂酸甲酯摩尔比>老化温度>磺化温度>老化时间。优化结果显示，当磺化温度为86.4℃、SO₃与硬脂酸甲酯摩尔比为1.4、老化时间为47min、老化温度为90℃时，产品活性物含量达88.1%，相比工业合成提高了5%左右。建立了微反应器体系下脂肪酸甲酯SO₃磺化反应的动力学方程式，拟合出动力学相关参数，为实现工艺过程可控、产物预测及反应器结构优化提供依据。采用NSGA2算法对工艺进行双目标优化，结合TOPSIS方法从Pareto解集中选出最佳工艺方案。

关键词: 微反应器, 气液磺化反应, 响应面法, 反应动力学, 优化设计

Abstract:

The internal temperature distributes unevenly for the synthesis of methyl ester sulfonate in falling film reactor, leading to the decrease in productivity. Considering the efficient reaction characteristics of microreactor, the present paper attempted to synthesize methyl ester sulfonate in a T-type microreactor. The effects of temperature, molar ratio, aging time, aging temperature and other factors on the content of active substances of the product (the mass fraction of the active content of the product ) in the sulfonation reaction process were investigated. The response surface method was used to explore the influence degree and interaction of the above influencing factors on the content of active substances, and the process conditions were optimized on this basis. According to the analysis, the influence order of above factors on the content of active substances was: molar ratio of sulfur trioxide to methyl stearate>aging temperature>sulfonation temperature>aging time. The optimization results showed that when the sulfonation temperature was 86.4℃, the molar ratio of SO₃ to methyl stearate was 1.4, the aging time was 47min and the aging temperature was 90℃, the active content of the product was 88.1%, which was about 5% higher than that of industrial synthesis. The kinetic equation of SO₃ sulfonation reaction of methyl ester in microreactor system was established and the kinetic parameters were fitted, which provided the basis for the process control, product prediction and reactor structure optimization. The NSGA2 algorithm was used to proceed double-target optimization for the reaction process, and combing the TOPSIS method, the best process conditions were selected from the Pareto solution set.

Key words: microreactor, gas-liquid sulfonation reaction, response surface method, reaction kinetics, optimal design

中图分类号:

TQ031.2

胡恒, 徐娜, 李梓良, 于嘉朋, 李旭, 张玮. T型微反应器中合成脂肪酸甲酯磺酸盐动力学及工艺优化[J]. 化工进展, 2024, 43(12): 6634-6644.

HU Heng, XU Na, LI Ziliang, YU Jiapeng, LI Xu, ZHANG Wei. Kinetics and process optimization of synthesis of methyl ester sulfonate in T-type microreactor[J]. Chemical Industry and Engineering Progress, 2024, 43(12): 6634-6644.

图/表 17

参考文献 33

1	SIWAYANAN Parthiban, BAN Zhen hong, ZHANG Xinchi, et al. α-sulfo fatty methyl ester sulfonate: A review on chemistry, processing technologies, performance, and applications in laundry detergents[J]. Journal of Surfactants and Detergents, 2021, 24(3): 385-399.
2	宋金玉, 彭志强, 孙笑宇. 脂肪酸甲酯磺酸盐的性能测试及其在洗衣液中的应用[J]. 日用化学工业, 2018, 48(12): 691-694.
	SONG Jinyu, PENG Zhiqiang, SUN Xiaoyu. The performance test of fatty acid methyl ester sulfonate and its application in the liquid detergent[J]. China Surfactant Detergent & Cosmetics, 2018, 48(12): 691-694.
3	YAVRUKOVA Veronika I, SHANDURKOV Dimitar N, MARINOVA Krastanka G, et al. Cleaning ability of mixed solutions of sulfonated fatty acid methyl esters[J]. Journal of Surfactants and Detergents, 2020, 23(3): 617-627.
4	Yee Seng LIM, BAHARUDIN Nur Bazlina, Yee Wei UNG. Methyl ester sulfonate: A high-performance surfactant capable of reducing builders dosage in detergents[J]. Journal of Surfactants and Detergents, 2019, 22(3): 549-558.
5	YUSUFF Adeyinka S, BODE-OLAJIDE Favour B. Comparing the performances of different sulfonating agents in sulfonation of methyl esters synthesized from used cooking oil[J]. Tenside Surfactants Detergents, 2023, 60(4): 277-285.
6	RUSSO Vincenzo, MILICIA Antonio, DI SERIO Martino, et al. Falling film reactor modelling for sulfonation reactions[J]. Chemical Engineering Journal, 2019, 377: 120464.
7	Arkadiusz CHRUŚCIEL, HRECZUCH Wiesław. Kinetic modelling and improvement of the ageing step of industrial alkylbenzene sulfonation process[J]. Chemical Engineering and Processing-Process Intensification, 2022, 181: 109143.
8	IVANCHINA Emiliya, IVASHKINA Elena, DOLGANOVA Irena, et al. Linear alkylbenzenes sulfonation: Design of film reactor and its influence on the formation of deactivating components[J]. Journal of Surfactants and Detergents, 2020, 23(6): 1007-1015.
9	AGHEL Babak, HEIDARYAN Ehsan, SAHRAIE Sasan, et al. Application of the microchannel reactor to carbon dioxide absorption[J]. Journal of Cleaner Production, 2019, 231: 723-732.
10	王彦谦, 王远洋. 微反应器中费托合成的研究进展[J]. 化工进展, 2021, 40(S2): 185-191.
	WANG Yanqian, WANG Yuanyang. Research progress of Fischer-Tropsch synthesis in microreactor[J]. Chemical Industry and Engineering Progress, 2021, 40(S2): 185-191.
11	LING Weisong, ZHOU Wei, YU Wei, et al. Experimental investigation on thermal and hydraulic performance of microchannels with interlaced configuration[J]. Energy Conversion and Management, 2018, 174: 439-452.
12	DENG Jian, ZHANG Jisong, WANG Kai, et al. Microreaction technology for synthetic chemistry[J]. Chinese Journal of Chemistry, 2019, 37(2): 161-170.
13	GUO Shuai, CAO Jianyang, LIU Meiqi, et al. Intensification and kinetic study of trifluoromethylbenzen nitration with mixed acid in the microreactor[J]. Chemical Engineering and Processing-Process Intensification, 2023, 183: 109239.
14	张经纬, 周弋惟, 陈卓, 等. 微反应器内的有机合成前沿进展[J]. 化工学报, 2022, 73(8): 3472-3482.
	ZHANG Jingwei, ZHOU Yiwei, CHEN Zhuo, et al. Advances in frontiers of organic synthesis in microreactor[J]. CIESC Journal, 2022, 73(8): 3472-3482.
15	张家康, 张月成, 赵继全. 微通道反应器中精细化学品合成危险工艺研究进展[J]. 精细化工, 2023, 40(4): 728-740.
	ZHANG Jiakang, ZHANG Yuecheng, ZHAO Jiquan. Research progresses on hazardous processes for fine chemical synthesis in microchannel reactors[J]. Fine Chemicals, 2023, 40(4): 728-740.
16	XU Yiming, LI Ping, MA Haoran, et al. Multistep cascade continuous flow synthesis of AOS based on microreactor[J]. Chemical Engineering and Processing-Process Intensification, 2023, 192: 109505.
17	孟维军, 徐一鸣, 李平, 等. 微通道内连续合成十二烷基苯磺酸的响应面分析及混合过程模拟[J]. 化工进展, 2021, 40(11): 5998-6008.
	MENG Weijun, XU Yiming, LI Ping, et al. Response surface analysis and mixing process simulation of continuous synthesis of dodecylbenzene sulfonic acid in microchannels[J]. Chemical Industry and Engineering Progress, 2021, 40(11): 5998-6008.
18	XU Yiming, LIU Suli, MENG Weijun, et al. Continuous sulfonation of hexadecylbenzene in a microreactor[J]. Green Processing and Synthesis, 2021, 10(1): 219-229.
19	XIE Tianming, ZENG Changfeng, WANG Chongqing, et al. Preparation of methyl ester sulfonates based on sulfonation in a falling film microreactor from hydrogenated palm oil methyl esters with gaseous SO₃ [J]. Industrial & Engineering Chemistry Research, 2013, 52(10): 3714-3722.
20	XIE Yu, FENG Mingjian, ZHANG Min, et al. Kinetics model of piperacillin synthesis in a microreactor[J]. Chemical Engineering Science, 2022, 259: 117821.
21	ZHAO Hailing, LIU Sai’er, SHANG Minjing, et al. Direct oxidation of benzene to phenol in a microreactor: Process parameters and reaction kinetics study[J]. Chemical Engineering Science, 2021, 246: 116907.
22	ZHANG Hong, SHANG Minjing, SONG Yang, et al. Continuous synthesis of tetraalkylammonium-based ethyl sulphate ionic liquid and its kinetic study in microreactors[J]. AIChE Journal, 2019, 65(4): 1245-1255.
23	CHEN Yu, YU Jiayuan, YANG Yiqian, et al. A continuous process for cyclic carbonate synthesis from CO₂ catalyzed by the ionic liquid in a microreactor system: Reaction kinetics, mass transfer, and process optimization[J]. Chemical Engineering Journal, 2023, 455: 140670.
24	LIN Xiyan, WANG Kai, ZHOU Baiyang, et al. A microreactor-based research for the kinetics of polyvinyl butyral (PVB) synthesis reaction[J]. Chemical Engineering Journal, 2020, 383: 123181.
25	LI Guangxiao, LIU Saier, DOU Xiaoyong, et al. Synthesis of adipic acid through oxidation of K/a oil and its kinetic study in a microreactor system[J]. AIChE Journal, 2020, 66(9): e16289.
26	CHAUDHARI Pranava, THAKUR Amit K, KUMAR Rahul, et al. Comparison of NSGA-‍Ⅲ with NSGA-‍Ⅱ for multi objective optimization of adiabatic styrene reactor[J]. Materials Today: Proceedings, 2022, 57: 1509-1514.
27	KOSE Haluk Anil, YILDIZELI Alperen, CADIRCI Sertac. Parametric study and optimization of microchannel heat sinks with various shapes[J]. Applied Thermal Engineering, 2022, 211: 118368.
28	SHANMUGAM Mathiyazhagan, SIRISHA MAGANTI Lakshmi. Multi-objective optimization of parallel microchannel heat sink with inlet/outlet U, I, Z type manifold configuration by RSM and NSGA-‍Ⅱ[J]. International Journal of Heat and Mass Transfer, 2023, 201: 123641.
29	WANG Guilian, DING Guifu, LIU Rui, et al. Multi-objective optimization of a bidirectional-ribbed microchannel based on CFD and NSGA-‍Ⅱ genetic algorithm[J]. International Journal of Thermal Sciences, 2022, 181: 107731.
30	徐一鸣, 袁华, 刘素丽, 等. 微通道反应器中工业混合直链烷基苯磺酸盐的连续合成工艺研究[J]. 化工学报, 2022, 73(3): 1184-1193.
	XU Yiming, YUAN Hua, LIU Suli, et al. Study on the continuous synthesis process of industrial mixed linear alkyl benzene sulfonates in a microchannel reactor[J]. CIESC Journal, 2022, 73(3): 1184-1193.
31	YUSUFF Adeyinka S, PORWAL Jyoti, BHONSLE Aman K, et al. Valorization of used cooking oil as a source of anionic surfactant fatty acid methyl ester sulfonate: Process optimization and characterization studies[J]. Biomass Conversion and Biorefinery, 2023, 13(10): 8903-8914.
32	王嘉鑫. 甲苯磺化动力学及超重力磺化工艺研究[D]. 北京: 北京化工大学, 2022.
	WANG Jiaxin. Study on sulfonation kinetics and supergravity sulfonation process of toluene[D]. Beijing: Beijing University of Chemical Technology, 2022.
33	SENTHIL KANNAN V, LENIN K, NAVNEETHAKRISHNAN P. Machining parameters optimization in laser beam machining for micro elliptical profiles using TOPSIS method[J]. Materials Today: Proceedings, 2020, 21: 727-730.

序号	磺化温度（A）/℃	SO₃/MS摩尔比（B）	老化时间（C）/min	老化温度（D）/℃	实际值/%	预测值/%
1	1	-1	1	1	72.8	72.9
2	0	2	0	0	85.2	86.0
3	1	-1	-1	1	70.1	69.9
4	-1	-1	1	-1	50.8	53.2
5	0	0	0	2	86.1	86.3
6	-1	1	1	-1	75.6	76.0
7	0	-2	0	0	49.3	47.4
8	-2	0	0	0	66.5	64.2
9	0	0	0	0	75.8	74.9
10	1	1	-1	-1	78.1	78.1
11	0	0	0	-2	69.4	67.0
12	0	0	0	0	76.2	74.9
13	1	-1	1	-1	63.6	63.4
14	-1	-1	-1	-1	47.5	48.6
15	-1	-1	-1	1	59.6	60.1
16	0	0	-2	0	67.7	67.3
17	0	0	0	0	75.6	74.9
18	0	0	0	0	73.8	74.9
19	0	0	2	0	75	74.4
20	-1	1	-1	-1	71.2	71.9
21	0	0	0	0	74.3	74.9
22	0	0	0	0	73.9	74.9
23	1	1	1	1	89	88.1
24	1	1	1	-1	80	80.3
25	-1	1	-1	1	81.3	81.7
26	1	-1	-1	-1	59.8	60.7
27	2	0	0	0	77	78.3
28	-1	1	1	1	86.1	86.0
29	1	1	-1	1	87.3	85.7
30	-1	-1	1	1	64.8	65.0

序号	磺化温度（A）/℃	SO₃/MS摩尔比（B）	老化时间（C）/min	老化温度（D）/℃	实际值/%	预测值/%
1	1	-1	1	1	72.8	72.9
2	0	2	0	0	85.2	86.0
3	1	-1	-1	1	70.1	69.9
4	-1	-1	1	-1	50.8	53.2
5	0	0	0	2	86.1	86.3
6	-1	1	1	-1	75.6	76.0
7	0	-2	0	0	49.3	47.4
8	-2	0	0	0	66.5	64.2
9	0	0	0	0	75.8	74.9
10	1	1	-1	-1	78.1	78.1
11	0	0	0	-2	69.4	67.0
12	0	0	0	0	76.2	74.9
13	1	-1	1	-1	63.6	63.4
14	-1	-1	-1	-1	47.5	48.6
15	-1	-1	-1	1	59.6	60.1
16	0	0	-2	0	67.7	67.3
17	0	0	0	0	75.6	74.9
18	0	0	0	0	73.8	74.9
19	0	0	2	0	75	74.4
20	-1	1	-1	-1	71.2	71.9
21	0	0	0	0	74.3	74.9
22	0	0	0	0	73.9	74.9
23	1	1	1	1	89	88.1
24	1	1	1	-1	80	80.3
25	-1	1	-1	1	81.3	81.7
26	1	-1	-1	-1	59.8	60.7
27	2	0	0	0	77	78.3
28	-1	1	1	1	86.1	86.0
29	1	1	-1	1	87.3	85.7
30	-1	-1	1	1	64.8	65.0

来源	平方和	自由度	均方差	F	P	显著性
模型	3369.85	14	240.7	96.73	<0.0001	高度显著
A	299.63	1	299.63	120.42	<0.0001	高度显著
B	2231.08	1	2231.08	896.64	<0.0001	高度显著
C	74.91	1	74.91	30.1	<0.0001	高度显著
D	556.81	1	556.81	223.77	<0.0001	高度显著
AB	34.22	1	34.22	13.75	0.0021	高度显著
AC	3.61	1	3.61	1.45	0.2471	—
AD	5.06	1	5.06	2.03	0.1742	—
BC	0.3025	1	0.3025	0.1216	0.7322	—
BD	2.89	1	2.89	1.16	0.2982	—
CD	0.0625	1	0.0625	0.0251	0.8762	—
A²	23.57	1	23.57	9.47	0.0077	高度显著
B²	115.5	1	115.5	46.42	<0.0001	高度显著
C²	28.93	1	28.93	11.63	0.0039	高度显著
D²	5.2	1	5.2	2.09	0.1688	不显著
残差	37.32	15	2.49	—	—	—
失拟项	31.77	10	3.18	2.86	0.1288	不显著
纯误差	5.55	5	1.11	—	—	—
总和	3407.17	29	—	—	—	—

来源	平方和	自由度	均方差	F	P	显著性
模型	3369.85	14	240.7	96.73	<0.0001	高度显著
A	299.63	1	299.63	120.42	<0.0001	高度显著
B	2231.08	1	2231.08	896.64	<0.0001	高度显著
C	74.91	1	74.91	30.1	<0.0001	高度显著
D	556.81	1	556.81	223.77	<0.0001	高度显著
AB	34.22	1	34.22	13.75	0.0021	高度显著
AC	3.61	1	3.61	1.45	0.2471	—
AD	5.06	1	5.06	2.03	0.1742	—
BC	0.3025	1	0.3025	0.1216	0.7322	—
BD	2.89	1	2.89	1.16	0.2982	—
CD	0.0625	1	0.0625	0.0251	0.8762	—
A²	23.57	1	23.57	9.47	0.0077	高度显著
B²	115.5	1	115.5	46.42	<0.0001	高度显著
C²	28.93	1	28.93	11.63	0.0039	高度显著
D²	5.2	1	5.2	2.09	0.1688	不显著
残差	37.32	15	2.49	—	—	—
失拟项	31.77	10	3.18	2.86	0.1288	不显著
纯误差	5.55	5	1.11	—	—	—
总和	3407.17	29	—	—	—	—

T/K	k₁	T^-1/10^-3K^-1	lnk₁
333.15	3.89113	3.00165	1.3587
338.15	4.41761	2.95727	1.4856
343.15	4.74267	2.91418	1.5566
348.15	5.42322	2.87232	1.6907
353.15	6.38659	2.83166	1.8542