文献类型：期刊文献

英文题名：Perspective on artificial intelligence for carbon capture utilization and storage (CCUS) in Petrochemical Industry

作者：Ma, Jin[1];Han, Yide[2];Wang, Meihong[1,2];Zhong, Weimin[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Sheffield, Sch Chem Mat & Biol Engn, Sheffield S1 3JD, England

年份：2025

卷号：16

外文期刊名：CARBON CAPTURE SCIENCE & TECHNOLOGY

收录：WOS:【ESCI(收录号:WOS:001562208100002)】；

基金：Professor Meihong Wang is an Advisory Editor of the journal; however, the editor had no access to the peer review or editorial process for this article at any point. The UK authors would like to thank the financial support of the EU RISE project OPTIMAL (Ref: Grant Agreement No:101007963) and CATALYSE (Ref: Grant Agreement No: 10183092) .

语种：英文

外文关键词：Carbon capture; utilisation and storage (CCUS); Petrochemical industry; Artificial Intelligence; Solvent selection and design; Catalyst Design; Sustainability; Carbon Capture; CO 2 Utilisation

摘要：The energy-intensive petrochemical industry contributes 14 % of global industrial emissions. In the face of climate change, there is an urgent need for the petrochemical industry transition to low carbon manufacturing. Deployment of carbon capture, utilization and storage (CCUS) technologies can effectively reduce carbon emissions from the petrochemical industry. However, the large-scale deployment of CCUS faces the obstacles of high energy consumption and high cost. Artificial intelligence (AI) has shown great potential to accelerate the large-scale deployment of CCUS in the petrochemical industry. Nevertheless, most AI-based approaches are still largely at the research stage and not yet widely adopted in industrial practice. This paper explores four aspects of AI for petrochemical industry to reduce CO2 emission, including the solvent selection and design for carbon capture, catalyst design for CO2 utilisation, hybrid process modelling for optimal design and operation, and life cycle sustainability assessment. We evaluate different promising approaches for AI in each aspect and highlight our key findings, with the goal to accelerate the petrochemical industry transition to carbon neutrality.