Enhancement Strategies of Calcium Looping Technology and CaO-Based Sorbents for Carbon Capture

被引:2
作者
Wang, Zirui [1 ]
Ma, Chenyang [2 ]
Harrison, Alexander [1 ]
Alsouleman, Khulud [3 ]
Gao, Mingchen [1 ]
Huang, Zi [1 ]
Chen, Qicheng [4 ]
Nie, Binjian [1 ]
机构
[1] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England
[2] Univ Oxford, Dept Comp Sci, Oxford OX1 3QG, England
[3] Tech Univ Berlin, Energy Proc Engn & Convers Technol Renewable Energ, D-13353 Berlin, Germany
[4] Northeast Elect Power Univ, Sch Energy & Power Engn, Jilin 132012, Peoples R China
基金
英国工程与自然科学研究理事会;
关键词
calcium looping; CaO-based sorbents; carbon capture; machine learning; TEMPERATURE CO2 CAPTURE; RANDOM PORE MODEL; DIOXIDE CAPTURE; ENERGY-STORAGE; ORGANIC-ACIDS; PILOT-PLANT; BY-PRODUCT; PERFORMANCE; LIMESTONE; POWER;
D O I
10.1002/smll.202412463
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
As global warming intensifies and energy resources deplete, carbon capture and sustainable energy conversion technologies gain increasing importance. Among these, calcium looping (CaL) technology has demonstrated promising cost-effectiveness and ease of integration with other systems. However, severe sintering of CaO-based sorbents occurs during cyclic carbonation and calcination, resulting in a significant decrease in CO2 capture capacity and stability. This paper reviews enhancement strategies in aggregate for synthetic CaO-based sorbents over the past 10 years, compiling a tabular dataset of 1042 reported materials, to compare the effects of synthesis methods and operation conditions on decay rate and CO2 capture capacity. Sol-gel, combustion, and template synthesis methods are recommended for producing high porosity CaO-based sorbents. The calcium precursors and organic acids used during synthesis, and addition of dopants, also play important roles in affecting the sorbent performance. This paper also examines the relationship between material synthesis, operation conditions, and performance of CaO-based sorbents to determine the feasibility of applying machine learning technology in materials development. This paper also discusses several possible artificial intelligence strategies with potential for designing innovative CaO-based sorbents suitable for long-term industrial applications, with the XGBoost model providing promising predictive capacity, particularly when working with relatively small, tabular, datasets.
引用
收藏
页数:17
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