Green ammonia to Hydrogen: Reduction and oxidation catalytic processes

被引:18
|
作者
Mashhadimoslem, Hossein [1 ]
Khosrowshahi, Mobin Safarzadeh [2 ]
Delpisheh, Mostafa [3 ]
Convery, Caillean [3 ]
Rezakazemi, Mashallah [1 ,4 ]
Aminabhavi, Tejraj M. [1 ,5 ]
Kamkar, Milad [1 ]
Elkamel, Ali [1 ,6 ]
机构
[1] Univ Waterloo, Chem Engn Dept, Waterloo, ON N2L 3G1, Canada
[2] Iran Univ Sci & Technol, Sch Adv Technol, Nanotechnol Dept, Tehran, Iran
[3] Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, England
[4] Shahrood Univ Technol, Fac Chem & Mat Engn, Shahrood, Iran
[5] KLE Technol Univ, Ctr Energy & Environm, Sch Adv Sci, Hubballi 580031, Karnataka, India
[6] Khalifa Univ, Dept Chem Engn, Abu Dhabi, U Arab Emirates
关键词
Ammonia decomposition; Hydrogen production; Reforming; Biomass; Machine Learning; TRANSITION-METAL SURFACES; NH3; DECOMPOSITION; CARBON NANOTUBES; H-2; PRODUCTION; GENERATION; CARRIERS; STORAGE; DESIGN; INFRASTRUCTURE; EFFICIENCY;
D O I
10.1016/j.cej.2023.145661
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Green ammonia, produced at ambient conditions, has received significant attention as a carrier of hydrogen for energy storage and transport. Reduction and oxidation catalytic decomposition of ammonia at atmospheric pressure and low temperatures suggests remarkable achievements over thermochemical processes requiring elevated pressures and temperatures. The present work examines various routes to produce hydrogen from ammonia, including those that employ fossil and non-fossil sources (biomass and ammonia), along with current procedures for ammonia decomposition and technical challenges. Ammonia decomposition methods, including catalytic membranes reactors, microchannel reactors, thermochemical energy, non-thermal plasma, solar-driven decomposition, isotope analysis, and electrolysis, have been investigated as potential techniques for producing hydrogen. In addition, ammonia decomposition methods applied with catalysts and hydrogen carrier challenges are also reviewed. Technical challenges and recommendations are provided to evaluate the potential usage of ammonia in the future energy sector. The role of machine learning and artificial intelligence in ammonia decomposition is highlighted, which enables the simulation of the reaction mechanisms to create novel, highperformance catalysts to minimize trial and error approaches in the ammonia energy sector.
引用
收藏
页数:19
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