Optimization and analysis of hydrogen liquefaction process based on dual mixed refrigerant deep-cooling

doi:10.11949/0438-1157.20250074

Abstract

Abstract:

To reduce the specific energy consumption of hydrogen liquefaction process and improve the utilization rate of cold energy in the deep-cooling stage, a novel cascade hydrogen liquefaction process based on dual mixed refrigerant deep-cooling is proposed. The process uses mixed refrigerant for pre-cooling and cascade double mixed refrigerant reverse Brayton cycles for deep-cooling. The process produces 300 t/d, and the para-hydrogen concentration of the product is more than 99%. The process was simulated using Aspen HYSYS software, and the genetic algorithm was called in Matlab to optimize the key parameters of the process with specific energy consumption as the objective function. The optimization results show that the specific energy consumption of the proposed process is 6.07 kWh/kg. The composite curves of heat exchanger are more matched, with a minimum internal temperature approach of 1.0—1.5℃. This process provides reference for the optimization and improvement of large-scale hydrogen liquefaction processes.

Key words: hydrogen, liquefaction, genetic algorithm, optimization, exergy analysis

摘要：

为降低氢液化过程的比能耗并提高深冷阶段的冷能利用率，提出一种新型的基于双混合工质深冷的级联氢液化工艺。该工艺使用混合工质预冷，通过级联的双混合工质逆布雷顿循环深冷。工艺的液氢产量为300 t/d，通过六级正仲氢转化后，产品仲氢浓度大于99%。利用Aspen HYSYS软件对工艺进行模拟，并在Matlab中调用遗传算法以比能耗为目标函数对工艺的关键参数进行优化。基于优化结果进行㶲分析和换热器性能分析，结果表明：优化后该工艺的比能耗为6.07 kWh/kg，㶲效率为53.01%。各级换热器的冷热流复合曲线更加匹配，最小换热温差均在1.0~1.5℃。该工艺设计可为大规模氢液化工艺的优化和改进提供参考。

关键词: 氢, 液化, 遗传算法, 优化, ?分析

CLC Number:

TQ 116.2

Lu LIU, Wenyue WANG, Teng WANG, Tai WANG, Xinyu DONG, Jiancheng TANG, Shaoheng WANG. Optimization and analysis of hydrogen liquefaction process based on dual mixed refrigerant deep-cooling[J]. CIESC Journal, 2025, 76(9): 4933-4943.

刘璐, 王文玥, 王腾, 王太, 董新宇, 汤建成, 王少恒. 基于双混合工质深冷的氢液化工艺优化与分析[J]. 化工学报, 2025, 76(9): 4933-4943.

Figures/Tables 12

Fig.1 Process flowchart of hydrogen liquefaction process based on dual mixed refrigerant for deep-cooling

Table 1 Composition of mixed refrigerant

组分/%(mol)	MR1循环	MR2循环	MR3循环
CH₄	17	—	—
C₂H₄	16	—	—
C₂H₆	7	—	—
C₃H₈	18	—	—
n-C₄H₁₀	2	—	—
n-C₅H₁₂	15	—	—
H₂	1	87	18
N₂	16	—	—
R-14	8	—	—
He	—	—	81
Ne	—	13	1

Fig.2 Diagram of the optimization algorithm

Table 2 The setting parameters of genetic algorithms

参数	数值
种群大小	400
最大进化代数	600
交叉概率	0.8
变异率	0.2

Fig.3 The convergence curve of genetic algorithms

Table 3 Comparison of decision variables before and after optimization

决策变量	优化前	优化后	决策变量	优化前	优化后
p_PR1/kPa	140	199.5	m_PR1/(kg/s)	91.5	102.70
p_PR2/kPa	600	647.5	m_N9a/(kg/s)	11.8	11.64
p_PR5a/kPa	1600	1508.0	m_N10a/(kg/s)	15.8	14.92
p_N1/kPa	104	108.7	m_M10a/(kg/s)	17.6	21.23
p_N2/kPa	240	225.7	m_M11a/(kg/s)	8.1	12.90
p_N4/kPa	470	492.3	T_M10c/℃	-248.1	-246.7
p_M1/kPa	105	120.2	T_M11c/℃	-254.0	-253.3
p_M2/kPa	250	238.4	T_N9c/℃	-219.5	-219.0
p_M4/kPa	460	474.5	T_N10c/℃	-236.5	-235.7

Table 4 The specific energy consumption and output power of each device before and after optimization

设备	功率/kW		比能耗/(kWh/kg)	设备	功率/kW		比能耗/(kWh/kg)
设备	优化前	优化后	优化后	设备	优化前	优化后	优化后
Com-1	7019	8609	0.69	Exp-1	1771	1778	0.14
Com-2	7318	5423	0.44	Exp-2	1591	1548	0.12
Com-3	15910	13630	1.10	Exp-3	1816	1678	0.14
Com-4	13450	14700	1.19	Exp-4	766	754	0.06
Com-5	19430	19100	1.54	W_Net	78773	75094	6.07
Com-6	21590	19390	1.57	COP	0.198	0.208	—

Fig.4 Cold and hot composite curves of heat exchangers

Table 5 Exergy balance equation of main equipment

设备	㶲平衡方程
压缩机	$Δ E C o m = E i n - E o u t + W C o m = m C o m (e C o m_i n - e C o m_o u t) + W C o m$
换热器	$Δ E H E X = E i n - E o u t = ∑ j = 1 n m H E X - j (e H E X - j_i n - e H E X - j_o u t)$
冷却器	$Δ E C o o l e r = E i n - E o u t = m C o o l e r (e C o o l e r_i n - e C o o l e r_o u t)$
J-T 阀	$Δ E J - T = E i n - E o u t = m J - T (e J - T_i n - e J - T_o u t)$
膨胀机	$Δ E E x p = E i n - E o u t - W E x p = m E x p (e E x p_i n - e E x p_o u t) - W E x p$

Table 5 Exergy balance equation of main equipment

设备	㶲平衡方程
压缩机	$Δ E C o m = E i n - E o u t + W C o m = m C o m (e C o m_i n - e C o m_o u t) + W C o m$
换热器	$Δ E H E X = E i n - E o u t = ∑ j = 1 n m H E X - j (e H E X - j_i n - e H E X - j_o u t)$
冷却器	$Δ E C o o l e r = E i n - E o u t = m C o o l e r (e C o o l e r_i n - e C o o l e r_o u t)$
J-T 阀	$Δ E J - T = E i n - E o u t = m J - T (e J - T_i n - e J - T_o u t)$
膨胀机	$Δ E E x p = E i n - E o u t - W E x p = m E x p (e E x p_i n - e E x p_o u t) - W E x p$

Fig.5 The proportion of exergy loss of each type of equipment in the proposed process

Fig.6 Detailed exergy loss of each main equipment of the proposed process

Table 6 Performance comparison of hydrogen liquefaction processes

产量/(t/d)	深冷阶段制冷方式	是否级联	正仲氢转化类型	比能耗/(kWh/kg)	㶲效率/%	文献
90	单混合	否	—	6.47	45.50	[19]
100	多级联	否	绝热	7.69	39.50	[26]
300	双混合	否	等温	5.74	55.3	[27]
300	单混合	是	绝热	5.66	52.77	[29]
300	单混合	否	绝热	6.43	—	[30]
300	双混合	是	绝热	6.07	53.01	本文

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