低温甲醇洗碳捕集改进与优化

doi:10.16085/j.issn.1000-6613.2022-0063

摘要/Abstract

摘要：

减少CO₂排放对环境保护尤为重要。传统低温甲醇洗工艺中大量被贫甲醇吸收的CO₂由于被N₂稀释而直接作为尾气排放进大气中。为探究工艺改进空间，本研究基于Aspen Plus对一传统低温甲醇洗工艺进行模拟，物性方法选用CPA（Cubic-Plus-Association）模型并对该模型的二元交互系数进行回归修正，后与实际数据进行对比确保模型的准确性。对于工艺改进，首先在传统低温甲醇洗工艺的基础上采用四级增压热闪蒸和降压闪蒸相结合的方式进行一次改进以用于CO₂捕集，并对相关参数进行优化以进一步降低系统公用工程消耗。结果显示，虽然一次改进工艺的CO₂产量是传统工艺的3.3倍，但系统能量消耗增加了2.12%，系统㶲消耗增加了17.81%。接着，在一次改进工艺的基础上采用“半贫液+透平回收”相结合技术进一步进行节能改进，二次改进工艺的CO₂产量不仅与一次改进工艺相当，系统能量消耗和系统㶲消耗相比于传统低温甲醇洗工艺也分别降低了17.16%和5.85%。

关键词: 甲醇洗工艺, 模拟, CPA模型, CO₂捕集, 节能

Abstract:

Reducing CO₂ emissions is particularly important for environmental protection. In conventional Rectisol process, a large amount of CO₂, absorbed by poor methanol, is diluted by N₂ and directly discharged into the atmosphere as tail gas. In order to explore the space for process improvement, a conventional Rectisol process was simulated based on Aspen Plus. The physical property method selected the CPA (Cubic-Plus-Association) model, and the binary interaction coefficient of the model was corrected, and then compared with the practical data to ensure the accuracy of the model. For process improvement, firstly, the first improvement, based on the conventional Rectisol process, was proposed by combining four-stage pressurized hot flash and depressurized flash for CO₂ capture, and the related parameters were optimized to further reduce the system utilities consumption. The result showed that although the CO₂ production of the first improvement process was 3.3 times that of the conventional process, the system energy consumption increased by 2.12%, and the system exergy consumption increased by 17.81%. Next, further energy saving improvement was made on the basis of the first improvement process by adopting the combined technology of "semi-lean solution + turbine recovery". The CO₂ production of the second improvement process was not only equivalent to the first improvement process, but also the system energy consumption and exergy consumption reduce by 17.16% and 5.85% respectively compared with the conventional Rectisol process.

Key words: Rectisol process, simulated, CPA model, CO₂ capture, energy saving

中图分类号:

TQ546.5

张陆, 杨声. 低温甲醇洗碳捕集改进与优化[J]. 化工进展, 2022, 41(11): 6167-6175.

ZHANG Lu, YANG Sheng. Improvement and optimization of carbon capture via Rectisol[J]. Chemical Industry and Engineering Progress, 2022, 41(11): 6167-6175.

图/表 14

参考文献 25

1	DOTTORI F, SZEWCZYK W, CISCAR J C, et al. Increased human and economic losses from river flooding with anthropogenic warming[J]. Nature Climate Change, 2018, 8(9): 781-786.
2	LI Y, SHANG J H, ZHANG C, et al. The role of freshwater eutrophication in greenhouse gas emissions: a review[J]. Science of the Total Environment, 2021, 768: 144582.
3	MICHAELIDES E E. Thermodynamic analysis and power requirements of CO₂ capture, transportation, and storage in the ocean[J]. Energy, 2021, 230: 120804.
4	BUI M, ADJIMAN C S, BARDOW A, et al. Carbon capture and storage (CCS): the way forward[J]. Energy & Environmental Science, 2018, 11(5): 1062-1176.
5	王明坛, 谢圣林, 许子通. 二氧化碳捕集技术的现状与最新进展[J]. 当代化工, 2016, 45(5): 1002-1005.
	WANG Mingtan, XIE Shenglin, XU Zitong. Present state and latest development of CO₂ capture technology[J]. Contemporary Chemical Industry, 2016, 45(5): 1002-1005.
6	张娜. 低温甲醇洗技术及其在煤化工中的应用[J]. 化工设计通讯, 2021, 47(3): 11-12.
	ZHANG Na. Low-temperature methanol washing technology and its application in coal chemical industry[J]. Chemical Engineering Design Communications, 2021, 47(3): 11-12.
7	SCHOLES C A, ANDERSON C J, STEVENS G W, et al. Membrane gas separation-physical solvent absorption combined plant simulations for pre-combustion capture[J]. Energy Procedia, 2013, 37: 1039-1049.
8	RANKE G, WEISS H. Process for separating and recovering gaseous components from a gas mixture by physical washing: EP 79105255.8[P]. 1980-07-09.
9	YANG S, QIAN Y, YANG S Y. Development of a full CO₂ capture process based on the rectisol wash technology[J]. Industrial & Engineering Chemistry Research, 2016, 55(21): 6186-6193.
10	SHARMA I, HOADLEY A F A, MAHAJANI S M, et al. Multi-objective optimisation of a Rectisol™ process for carbon capture[J]. Journal of Cleaner Production, 2016, 119: 196-206.
11	GATTI M, MARTELLI E, MARECHAL F, et al. Review, modeling, Heat Integration, and improved schemes of Rectisol®-based processes for CO₂ capture[J]. Applied Thermal Engineering, 2014, 70(2): 1123-1140.
12	LIU X, YANG S Y, HU Z G, et al. Simulation and assessment of an integrated acid gas removal process with higher CO₂ capture rate[J]. Computers & Chemical Engineering, 2015, 83: 48-57.
13	佘红梅, 尹广华, 贾春临. 变压吸附技术提纯回收甲醇洗放空气中二氧化碳[J]. 大氮肥, 2009, 32(2): 129-132.
	SHE Hongmei, YIN Guanghua, JIA Chunlin. Using PSA technology to recover CO₂ from rectisol vented gas[J]. Large Scale Nitrogenous Fertilizer Industry, 2009, 32(2): 129-132.
14	刘都现, 韩振生. 浅析低温甲醇洗尾气中二氧化碳的回收利用[J]. 山东工业技术, 2016(11): 43.
	LIU Duxian, HAN Zhensheng. Discussion on recover and utilization of CO₂ from rectisol tail gas[J]. Shandong Industrial Technology, 2016(11): 43.
15	解寅珑. 基于某厂低温甲醇洗工艺装置模拟与优化改进方案研究[D]. 西安: 西北大学, 2020.
	XIE Yinlong. The study on process simulation and optimization improvement scheme of rectisol equipment of on A plant[D]. Xi’an: Northwest University, 2020.
16	MOORTGAT J. Reservoir simulation with the cubic plus (cross-) association equation of state for water, CO₂, hydrocarbons, and tracers[J]. Advances in Water Resources, 2018, 114: 29-44.
17	WANG W G, WANG J F, LU Z J, et al. Exergoeconomic and exergoenvironmental analysis of a combined heating and power system driven by geothermal source[J]. Energy Conversion and Management, 2020, 211: 112765.
18	LIU J, ZHANG X S, LIN Z, et al. Exergy and energy analysis of a novel dual-chilling-source refrigerating system applied to temperature and humidity independent control[J]. Energy Conversion and Management, 2019, 197: 111875.
19	HINDERINK A P, KERKHOF F P J M, LIE A B K, et al. Exergy analysis with a flowsheeting simulator—I. Theory; calculating exergies of material streams[J]. Chemical Engineering Science, 1996, 51(20): 4693-4700.
20	张旭. 基于㶲分析的焦炉煤气甲烷化工艺优化[D]. 马鞍山: 安徽工业大学, 2019.
	ZHANG Xu. Optimization of coke oven gas methanation process based on exergy analysis[D]. Ma’anshan: Anhui Universit of Technology, 2019.
21	傅秦生. 能量系统的热力学分析方法[M]. 西安: 西安交通大学出版社, 2005.
	FU Qinsheng. Methods for thermodynamic analysis of energy systems[M]. Xi’an: Xi’an Jiaotong University Press, 2005.
22	YU M X, CUI P Z, WANG Y Let al. Advanced exergy and exergoeconomic analysis of cascade absorption refrigeration system driven by low-grade waste heat[J]. ACS Sustainable Chemistry & Engineering, 2019, 7(19): 16843-16857.
23	全国能源基础与管理标准化技术委员会. 能量系统㶲分析技术导则： [S]. 北京: 中国标准出版社，2005.
	Energy Fundamentals and Management. Technical guides for exergy analysis in energy system: [S]. Beijing: Standards Press of China, 2005.
24	刘振学, 王力. 实验设计与数据处理[M]. 2版.北京: 化学工业出版社, 2015.
	LIU Zhenxue, WANG Li. Experimental design and data processing[M]. 2nd ed. Beijing: Chemical Industry Press, 2015.
25	DENG J Q, CAO Z, ZHANG D B, et al. Integration of energy recovery network including recycling residual pressure energy with pinch technology[J]. Chinese Journal of Chemical Engineering, 2017, 25(4): 453-462.

物流	组分	含量
净化气	H₂S	<0.1μL/L
CO₂产品气	CO₂	>98.5%
CO₂产品气	H₂S	<5μL/L
废气	H₂S	<60μL/L
贫甲醇	H₂S	<1μL/L
Claus气	H₂S	>35%
废水	CH₃OH	<0.05%

物流	组分	含量
净化气	H₂S	<0.1μL/L
CO₂产品气	CO₂	>98.5%
CO₂产品气	H₂S	<5μL/L
废气	H₂S	<60μL/L
贫甲醇	H₂S	<1μL/L
Claus气	H₂S	>35%
废水	CH₃OH	<0.05%

项目	组分 i	组分 j	AIJ	BIJ
CPAKIJ	CH₃OH	CO₂	-0.122174	0.205185
	CH₃OH	H₂S	0.0381023	-0.065128
	CH₃OH	H₂	0.552763	-1.79925
CPAVIJ	CH₃OH	CO₂	0.0147967	0.0818379
	CH₃OH	H₂S	-9.00×10^-5	-0.0199771
	CH₃OH	H₂	-0.348543	0.385507

项目	组分 i	组分 j	AIJ	BIJ
CPAKIJ	CH₃OH	CO₂	-0.122174	0.205185
	CH₃OH	H₂S	0.0381023	-0.065128
	CH₃OH	H₂	0.552763	-1.79925
CPAVIJ	CH₃OH	CO₂	0.0147967	0.0818379
	CH₃OH	H₂S	-9.00×10^-5	-0.0199771
	CH₃OH	H₂	-0.348543	0.385507

物流号	温度/℃	压力/bar	摩尔流量/kmol·h^-1	摩尔分数
物流号	温度/℃	压力/bar	摩尔流量/kmol·h^-1	H₂	N₂	CO	AR	CH₄	CO₂	H₂S	COS	CH₃OH	H₂O
1	-39.38	54.75	12206.00	0.6619	0.0033	0.3072	0.0014	0.0011	0.0250	—	—	100×10^-6	—
2	-51.28	3.00	1547.03	0.0008	0.0023	0.0044	25.06×10^-6	68.04×10^-6	0.9923	1.13×10^-6	—	0.0002	0.12×10^-6
3	-61.35	1.85	4259.37	0.0013	0.1335	0.0072	41.43×10^-6	0.0001	0.8577	1.27×10^-6	—	0.0001	—
4	98.50	3.35	15067.00	—	—	—	—	—	—	0.39×10^-6	—	0.9950	0.0050
5	139.88	3.60	709.55	—	—	—	—	—	—	—	—	100×10^-6	0.9999