Optimization and adjustment of operating parameters for green ammonia production under variable load conditions

doi:10.11949/0438-1157.20241196

Abstract

Abstract:

Ammonia is one of the most produced inorganic chemicals in the world, and the green ammonia process mainly relies on the use of renewable energy such as solar energy and wind energy to generate water electrolysis to produce green hydrogen, and then uses iron-based catalysts to promote the catalytic reaction of green hydrogen and nitrogen under high temperature and high pressure conditions to synthesize green ammonia. However, the inherent intermittency and seasonality of renewable energy generation directly lead to instability in the supply of hydrogen, a key feedstock for ammonia synthesis, thereby preventing continuous operation of green ammonia production at full load. Therefore, to effectively adapt to the fluctuating energy supply, it is essential to develop a flexible operating strategy that ensures the continuity and stability of production. In this study, a comprehensive mathematical model incorporating key process units, including water electrolysis for hydrogen production, gas compression, ammonia synthesis, and product separation, was developed by integrating mechanistic and surrogate models. This model was used to thoroughly analyze the steady-state operating range and operational flexibility of the green ammonia production system, particular emphasis was placed on examining the impact of varying operational parameters, such as the H₂/N₂ and the inert gas content, under partial load conditions. The results indicate that the flexible production of green ammonia can be realized in the load range of 30% to 100% by adjusting the H₂/N₂ and the inert gas content.

Key words: renewable energy, green ammonia, simulation, surrogate model, process control, flexibility

摘要：

氨是全球生产量最大的无机化学品之一，而绿氨工艺主要依赖于利用太阳能、风能等可再生能源发电进行水电解以制备绿氢，随后在高温高压条件下，采用铁基催化剂促进绿氢与氮气的催化反应，从而合成绿氨。然而，可再生能源发电固有的间歇性和季节性，直接导致了合成氨原料氢气供应的不稳定，绿氨生产无法持续在满负荷下运行。因此，为了使绿氨生产能够有效地适应这种波动的能源供给情况，需要开发出一套灵活的操作策略以确保生产的连续性和稳定性。通过结合机理模型和代理模型，构建一个包含电解水制氢、气体压缩、合成氨反应以及产物分离等关键工艺步骤在内的数学模型，深入分析了绿氨生产系统的稳态运行范围以及操作灵活性，重点分析了部分负荷下H₂/N₂、惰性气体含量等操作参数变化对整个绿氨生产系统的影响。结果表明，通过对H₂/N₂和惰性气体含量这些关键参数进行适当调整，可以在30%~100%的负荷范围内实现绿氨的柔性生产。

关键词: 再生能源, 绿氨, 模拟, 代理模型, 过程控制, 柔性

CLC Number:

TQ 021.8

Min XIONG, Dongmei LIU, Zhichao WANG, Li ZHOU, Xu JI. Optimization and adjustment of operating parameters for green ammonia production under variable load conditions[J]. CIESC Journal, 2025, 76(6): 2791-2801.

熊敏, 刘冬妹, 王智超, 周利, 吉旭. 变负荷条件下绿氨生产操作参数的调控与优化[J]. 化工学报, 2025, 76(6): 2791-2801.

Figures/Tables 11

Fig.1 Schematic of green ammonia production based on the Haber-Bosch process

Fig.2 Construction steps of the surrogate model

Table 1 Physical properties data for H2， N2， NH3 and Ar

组分	T_c/K	P_c×10^-6/Pa	ω
H₂	33.2	1.297	-0.220
N₂	126.2	3.394	0.040
NH₃	405.6	11.280	0.250
Ar	150.8	4.874	-0.004

Table 2 Domains of the input variables for the catalyst bed

输入变量	下限	上限
$m b, H 2 i n$ /(kg/h)	550	11000
$m b, N 2 i n$ /(kg/h)	2500	50000
$m b, N H 3 i n$ /(kg/h)	200	18000
$m b, A r i n$ /(kg/h)	20	200
$T b i n$ /K	520	700
$P b i n$ /bar	100	200
$V b$ /m³	1	15

Table 2 Domains of the input variables for the catalyst bed

输入变量	下限	上限
$m b, H 2 i n$ /(kg/h)	550	11000
$m b, N 2 i n$ /(kg/h)	2500	50000
$m b, N H 3 i n$ /(kg/h)	200	18000
$m b, A r i n$ /(kg/h)	20	200
$T b i n$ /K	520	700
$P b i n$ /bar	100	200
$V b$ /m³	1	15

Table 3 Validation results of the catalyst bed model

输出变量	RMSE	R²
$α b$	1.14×10^-2	0.939
$T b o u t$ /K	2.40	0.939

Table 3 Validation results of the catalyst bed model

输出变量	RMSE	R²
$α b$	1.14×10^-2	0.939
$T b o u t$ /K	2.40	0.939

Fig.3 Residual plot of the surrogate model of the catalyst bed

Table 4 Domains of the input variables for the separation unit

输入变量	下限	上限
$F s p, H 2 i n$ /(kmol/h)	200	3800
$F s p, N 2 i n$ /(kmol/h)	50	1200
$F s p, N H 3 i n$ /(kmol/h)	150	1100
$F s p, A r i n$ /(kmol/h)	0.50	5.00
$P s p i n$ /bar	100	200
$T s p i n$ /K	263	283

Table 4 Domains of the input variables for the separation unit

输入变量	下限	上限
$F s p, H 2 i n$ /(kmol/h)	200	3800
$F s p, N 2 i n$ /(kmol/h)	50	1200
$F s p, N H 3 i n$ /(kmol/h)	150	1100
$F s p, A r i n$ /(kmol/h)	0.50	5.00
$P s p i n$ /bar	100	200
$T s p i n$ /K	263	283

Table 5 Validation results of the separation unit

输出变量	RMSE	R²
$F s p, H 2 l v a p$ /(kmol/h)	2.11×10^-2	0.979
$F s p, N 2 l v a p$ /(kmol/h)	5.16×10^-3
$F s p, N H 3 l v a p$ /(kmol/h)	1.21×10^-1
$F s p, A r l v a p$ /(kmol/h)	7.53×10^-4
$F s p, H 2 l l i q$ /(kmol/h)	6.27×10^-3
$F s p, N 2 l l i q$ /(kmol/h)	1.83×10^-3
$F s p, N H 3 l l i q$ /(kmol/h)	5.88×10^-2
$F s p, A r l l i q$ /(kmol/h)	1.48×10^-4

Table 5 Validation results of the separation unit

输出变量	RMSE	R²
$F s p, H 2 l v a p$ /(kmol/h)	2.11×10^-2	0.979
$F s p, N 2 l v a p$ /(kmol/h)	5.16×10^-3
$F s p, N H 3 l v a p$ /(kmol/h)	1.21×10^-1
$F s p, A r l v a p$ /(kmol/h)	7.53×10^-4
$F s p, H 2 l l i q$ /(kmol/h)	6.27×10^-3
$F s p, N 2 l l i q$ /(kmol/h)	1.83×10^-3
$F s p, N H 3 l l i q$ /(kmol/h)	5.88×10^-2
$F s p, A r l l i q$ /(kmol/h)	1.48×10^-4

Fig.4 Residual plot of the surrogate model of the separator unit

Fig.5 Pressure variation of catalyst beds at fixed operating conditions and part load

Fig.6 The H2/N2, inert gas content and the pressure of catalyst beds over the load range of 30% to 100%

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