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

Abstract

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.