Abstract
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 CO(2) 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 CO(2) 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.