变负荷条件下绿氨生产操作参数的调控与优化

doi:10.11949/0438-1157.20241196

摘要/Abstract

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

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

中图分类号:

TQ 021.8

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.

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.

图/表 11

图1 基于Haber-Bosch工艺的绿氨生产示意图

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

图2 代理模型的构建步骤

Fig.2 Construction steps of the surrogate model

表1 H2、N2、NH3和Ar的物性数据

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

表2 催化剂床层的输入变量范围

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

表2 催化剂床层的输入变量范围

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

表3 催化剂床层模型的验证结果

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

表3 催化剂床层模型的验证结果

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

图3 催化剂床层代理模型的残差图

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

表4 分离单元的输入变量范围

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

表4 分离单元的输入变量范围

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

表5 分离单元模型的验证结果

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

表5 分离单元模型的验证结果

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

图4 分离单元代理模型的残差图

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

图5 固定操作条件，部分负荷下各催化剂床层的压力变化

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

图6 30%~100%负荷范围内各催化剂床层的压力以及相应的H2/N2、惰性气体含量

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

参考文献 34

[1]	Su C W, Pang L D, Qin M, et al. The spillover effects among fossil fuel, renewables and carbon markets: evidence under the dual dilemma of climate change and energy crises[J]. Energy, 2023, 274: 127304.
[2]	Li Y M, Alharthi M, Ahmad I, et al. Nexus between renewable energy, natural resources and carbon emissions under the shadow of transboundary trade relationship from South East Asian economies[J]. Energy Strategy Reviews, 2022, 41: 100855.
[3]	Rissman J, Bataille C, Masanet E, et al. Technologies and policies to decarbonize global industry: review and assessment of mitigation drivers through 2070[J]. Applied Energy, 2020, 266: 114848.
[4]	Olabi A G, Abdelkareem M A. Renewable energy and climate change[J]. Renewable and Sustainable Energy Reviews, 2022, 158: 112111.
[5]	Liu B T, Yuan Z H. Multistage distributionally robust design of a renewable source processing network under uncertainty[J]. Industrial & Engineering Chemistry Research, 2021, 60(21): 7883-7903.
[6]	Mahmoud M, Ramadan M, Olabi A G, et al. A review of mechanical energy storage systems combined with wind and solar applications[J]. Energy Conversion and Management, 2020, 210: 112670.
[7]	Aftab W, Usman A, Shi J M, et al. Phase change material-integrated latent heat storage systems for sustainable energy solutions[J]. Energy & Environmental Science, 2021, 14(8): 4268-4291.
[8]	Jiang Y H, Kang L X, Liu Y Z. Multi-objective design optimization of a multi-type battery energy storage in photovoltaic systems[J]. Journal of Energy Storage, 2021, 39: 102604.
[9]	Mehrpooya M, Pakzad P. Introducing a hybrid mechanical-chemical energy storage system: process development and energy/exergy analysis[J]. Energy Conversion and Management, 2020, 211: 112784.
[10]	Meng W L, Wang D L, Zhou H R, et al. Carbon dioxide from oxy-fuel coal-fired power plant integrated green ammonia for urea synthesis: process modeling, system analysis, and techno-economic evaluation[J]. Energy, 2023, 278: 127537.
[11]	Meng W L, Wang D L, Zhou H R, et al. Design and analysis for chemical process electrification based on renewable electricity: coal-to-methanol process as a case study[J]. Energy Conversion and Management, 2023, 292: 117424.
[12]	Huang R X, Kang L X, Liu Y Z. Renewable synthetic methanol system design based on modular production lines[J]. Renewable and Sustainable Energy Reviews, 2022, 161: 112379.
[13]	Choe C, Kim H, Lim H. Feasibility study of power-to-gas as simultaneous renewable energy storage and CO₂ utilization: direction toward economic viability of synthetic methane production[J]. Sustainable Energy Technologies and Assessments, 2023, 57: 103261.
[14]	曾悦, 王月, 张学瑞, 等. 可再生能源合成绿氨研究进展及氢-氨储运经济性分析[J]. 化工进展, 2024, 43(1): 376-389.
	Zeng Y, Wang Y, Zhang X R, et al. Research progress of green ammonia synthesis from renewable energy and economic analysis of hydrogen-ammonia storage and transportation[J]. Chemical Industry and Engineering Progress, 2024, 43(1): 376-389.
[15]	黄富林, 田烨玮, 师蓉. 绿氨的生产和发展趋势[J]. 肥料与健康, 2023, 50(5): 1-7.
	Huang F L, Tian Y W, Shi R. Production and development trends of green ammonia[J]. Fertilizer & Health, 2023, 50(5): 1-7.
[16]	Wang Q R, Guo J P, Chen P. Recent progress towards mild-condition ammonia synthesis[J]. Journal of Energy Chemistry, 2019, 36: 25-36.
[17]	Fahr S, Schiedeck M, Schwarzhuber J, et al. Design and thermodynamic analysis of a large-scale ammonia reactor for increased load flexibility[J]. Chemical Engineering Journal, 2023, 471: 144612.
[18]	Armijo J, Philibert C. Flexible production of green hydrogen and ammonia from variable solar and wind energy: case study of Chile and Argentina[J]. International Journal of Hydrogen Energy, 2020, 45(3): 1541-1558.
[19]	Wu Y, Zhao T T, Tang S, et al. Research on design and multi-frequency scheduling optimization method for flexible green ammonia system[J]. Energy Conversion and Management, 2024, 300: 117976.
[20]	Quintero-Masselski C, Portha J F, Falk L. Conception and optimization of an ammonia synthesis superstructure for energy storage[J]. Chemical Engineering Research and Design, 2022, 177: 826-842.
[21]	Khademi M H, Sabbaghi R S. Comparison between three types of ammonia synthesis reactor configurations in terms of cooling methods[J]. Chemical Engineering Research and Design, 2017, 128: 306-317.
[22]	Ostuni R, Zardi F. Method for load regulation of an ammonia plant: US9463983[P]. 2016-10-11.
[23]	Nayak-Luke R, Bañares-Alcántara R, Wilkinson I. “Green” ammonia: impact of renewable energy intermittency on plant sizing and levelized cost of ammonia[J]. Industrial & Engineering Chemistry Research, 2018, 57(43): 14607-14616.
[24]	Verleysen K, Parente A, Contino F. How sensitive is a dynamic ammonia synthesis process? Global sensitivity analysis of a dynamic Haber-Bosch process (for flexible seasonal energy storage)[J]. Energy, 2021, 232: 121016.
[25]	Cheema I I, Krewer U. Operating envelope of Haber-Bosch process design for power-to-ammonia[J]. RSC Advances, 2018, 8(61): 34926-34936.
[26]	吉旭, 周步祥, 贺革, 等. 大规模可再生能源电解水制氢合成氨关键技术与应用研究进展[J]. 工程科学与技术, 2022, 54(5): 1-11.
	Ji X, Zhou B X, He G, et al. Research review of the key technology and application of large-scale water electrolysis powered by renewable energy to hydrogen and ammonia production[J]. Advanced Engineering Sciences, 2022, 54(5): 1-11.
[27]	Sánchez M, Amores E, Abad D, et al. Aspen Plus model of an alkaline electrolysis system for hydrogen production[J]. International Journal of Hydrogen Energy, 2020, 45(7): 3916-3929.
[28]	López-Paniagua I, Rodríguez-Martín J, Sánchez-Orgaz S, et al. Step by step derivation of the optimum multistage compression ratio and an application case[J]. Entropy, 2020, 22(6): 678.
[29]	中国寰球化学工程公司, 中国石油化工总公司, 兰州石油化工设计院. 氮肥工艺设计手册: 气体压缩、氨合成、甲醇合成[M]. 北京: 化学工业出版社, 1989: 8-56.
	China Huanqiu Contracting & Engineering Limited Company, Group Sinopec, PetroChina Lanzhou Petrol-Chemical Industry Company. Nitrogen Fertilizer Process Design Manual: Gas Compression, Ammonia Synthesis, Methanol Synthesis[M]. Beijing: Chemical Industry Press, 1989: 8-56.
[30]	Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: a review[J]. Computers & Chemical Engineering, 2018, 108: 250-267.
[31]	Jin R, Chen W, Simpson T W. Comparative studies of metamodelling techniques under multiple modelling criteria[J]. Structural and Multidisciplinary Optimization, 2001, 23(1): 1-13.
[32]	Peng D Y, Robinson D B. A new two-constant equation of state[J]. Industrial & Engineering Chemistry Fundamentals, 1976, 15(1): 59-64.
[33]	陈钟秀, 顾飞燕, 胡望明. 化工热力学[M]. 3版. 北京: 化学工业出版社, 2012: 306-307.
	Chen Z X, Gu F Y, Hu W Y. Chemical Engineering Thermodynamics[M]. 3rd ed. Beijing: Chemical Industry Press, 2012: 306-307.
[34]	Sawant M R, Patwardhan A W, Gaikar V G, et al. Phase equilibria analysis of the binary N₂-NH₃ and H₂-NH₃ systems and prediction of ternary phase equilibria[J]. Fluid Phase Equilibria, 2006, 239(1): 52-62.