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为下一代碳捕获,利用和储存 (CCUS) 提供先进材料和人工智能的协同作用:一项审查

Somia Mazhar¹, Muhammad Waseem Mumtaz¹, Mohamed El Oirdi²

¹Department of Chemistry, University of Gujrat Gujrat Pakistan muhammad.waseem@uog.edu.pk.

|January 14, 2026

概括

碳捕获,利用和储存 (CCUS) 技术通过捕获二氧化碳 (CO2) 提供了一条通往净零排放的道路. 材料,人工智能和数字工具的进步正在提高CCUS的效率和可扩展性,以实现气候稳定.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.

背景情况:

工业和能源部门二氧化碳 (CO2) 排放量的增加对全球气候稳定构成重大威胁.
碳捕获,利用和储存 (CCUS) 技术对于实现"巴黎协定"规定的脱碳目标至关重要.
CCUS的目标是降低大气中的二氧化碳水平,并将捕获的碳转化为有价值的产品.

研究的目的:

审查最近材料和技术的进步,以提高CCUS过程的效率.
突出新型吸附剂,催化剂和储存方法在CCUS中的作用.
探索人工智能 (AI) 和机器学习 (ML) 在优化CCUS运营中的整合.

主要方法:

关于用于捕获二氧化碳的先进材料的文献综述,包括生物炭,纳米材料 (碳纳米管,石墨烯衍生物,纤维素纳米纤维,纳米孔状碳).
分析用于二氧化碳利用的材料,例如金属有机框架 (MOF) 和单原子催化剂 (SAC) 用于电化学还原.
检查二氧化碳储存的封存技术,包括矿物碳化,水合物形成和混合矩阵膜.
在材料选,预测建模和CCUS系统优化中评估AI/ML应用.
评估数字工具,如数字双胞胎和物联网的CCUS可靠性和可扩展性.

主要成果:

生物炭和纳米材料由于其多孔性和表面积,具有很高的二氧化碳捕获潜力.
基于石墨烯的MOF和SAC在电化学二氧化碳转化方面表现出有希望的选择性.
矿物质碳化,水合物形成和膜提供安全的二氧化碳捕获路径.
人工智能和机器学习通过预测建模和优化显著增强CCUS.
数字工具提高了CCUS部署的可靠性,可扩展性和可持续性.

结论:

由材料科学和数字智能驱动的CCUS技术对于净零能源转型具有变革性.
尽管面临成本,稳定性和可扩展性的挑战,CCUS的进步正在迅速出现.
材料创新和AI/ML之间的协同作用对CCUS的未来至关重要.

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