As the chemical industry seeks sustainable transformation, decarbonization requires intelligent solutions across multiple scales to enhance efficiency and reduce emissions. A research team led by Professor Xiaonan Wang at Tsinghua University has systematically reviewed AI-driven multi-scale smart systems for decarbonizing this energy-intensive sector. Published in Technology Review for Carbon Neutrality, the study explores innovations from materials discovery to industrial park optimization, highlighting the role of cross-scale modeling in addressing complex chemical processes. The findings provide insights for policymakers and industry leaders to advance smart, sustainable, and carbon-neutral chemical production.
The review highlights the role of AI in simplifying traditional mechanistic models, improving efficiency, and promoting resource conservation. At the microscopic scale, machine learning aids in material design and performance prediction, with emerging research focusing on uncovering underlying mechanisms. However, data reliability remains a major challenge. At the mesoscale, AI-driven process modeling accelerates industrial applications of decarbonization technologies, though scaling up digital integration remains a key hurdle. At the macro level, industrial symbiosis strategies optimize chemical parks by linking production with external markets and environmental factors, with digital twin technology enabling real-time operational adjustments.
Despite promising advancements, the application of intelligent technologies in the chemical industry remains largely in the research phase. Cross-scale modeling is essential for bridging molecular-scale innovations with industrial-scale applications, while AI-driven optimization enhances efficiency and sustainability. However, full-scale industrial implementation faces challenges spanning technical, economic, social, and ethical dimensions. These include data security, infrastructure compatibility, AI interpretability, workforce displacement, and regulatory considerations.
The study underscores the need for interdisciplinary collaboration and multi-stakeholder cooperation to address these challenges and accelerate the chemical industry’s transition to carbon neutrality. By integrating AI and digital technologies across scales, the industry can advance toward a more efficient, sustainable, and low-carbon future.
This work is supported by the National Key R&D Program of China (2023YFE0204600), Tsinghua University Initiative Scientific Research Program and The Special Project of National Natural Science Foundation (No. 42341204).
