气候变化领域集成服务门户(CCPortal): Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

CCPortal

DOI	10.1016/j.scitotenv.2024.170085
	Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities
	Al-Sakkari, Eslam G.; Ragab, Ahmed; Dagdougui, Hanane; Boffito, Daria C.; Amazouz, Mouloud
发表日期	2024
ISSN	0048-9697
EISSN	1879-1026
起始页码	917
卷号	917
英文摘要	Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/ future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data -driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.
英文关键词	Carbon capture utilization and sequestration; Artificial intelligence and machine Learning; Surrogate costing models; Reinforcement Learning; Simulation-based optimization; Material and process design; Life cycle assessment
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001175838200001
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305074
作者单位	Universite de Montreal; Polytechnique Montreal; Natural Resources Canada; CanmetENERGY; Universite de Montreal; Polytechnique Montreal
推荐引用方式 GB/T 7714	Al-Sakkari, Eslam G.,Ragab, Ahmed,Dagdougui, Hanane,et al. Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities[J],2024,917.
APA	Al-Sakkari, Eslam G.,Ragab, Ahmed,Dagdougui, Hanane,Boffito, Daria C.,&Amazouz, Mouloud.(2024).Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities.SCIENCE OF THE TOTAL ENVIRONMENT,917.
MLA	Al-Sakkari, Eslam G.,et al."Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities".SCIENCE OF THE TOTAL ENVIRONMENT 917(2024).