Recent advances in CO2 capture and utilization: From the perspective of process integration and optimization

过程(计算) 透视图(图形) 过程集成 计算机科学 工艺工程 工程类 人工智能 操作系统
作者
Nuo Wang,Jianzhao Zhou,Jingzheng Ren
出处
期刊:Renewable & Sustainable Energy Reviews [Elsevier BV]
卷期号:216: 115688-115688 被引量:35
标识
DOI:10.1016/j.rser.2025.115688
摘要

As the greenhouse effect intensifies, carbon capture and utilization (CCU) has gained increasing attention as a promising solution for decarbonizing fossil energy and industrial sectors. While traditional carbon capture methods, such as absorption and CO 2 hydrogenation, are relatively mature and have been applied industrially, there remains room for improvement in areas such as large-scale demonstration and energy efficiency. Emerging CO 2 capture and in-situ conversion technologies have garnered significant attention due to their potential to eliminate CO 2 transportation and reduce associated costs; however, further advancements in catalyst performance are required. Additionally, integrating CCU with systems such as organic Rankine cycles (ORC) can enhance overall performance by optimizing the utilization of thermal energy . Similarly, coupling CCU with renewable energy (RE) offers mutual benefits: it improves the environmental performance and operational costs of CCU while providing additional flexibility to accommodate renewable power generation . Nevertheless, critical research gaps persist, particularly in the development of accurate and efficient models for integrated systems . Although data-driven technologies present a promising solution, dynamic modeling and flexible operation of integrated CCU processes remain underexplored. This review underscores the need for advanced optimization strategies and system-level innovations to fully unlock the potential of CCU technologies in achieving sustainable decarbonization. • CCU technologies are summarized and comprehensively reviewed. • The current development progress of emerging ICCU technologies is reviewed. • System integration design offers a promising approach to enhance energy efficiency. • Data-driven technology shows superiority in model establishment and optimization.
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