@article{Chong2025, 
author = {Xuequn Chong and Lanyu Li and Chuan Zhang and Yingru Zhao and Markus Kraft and Xiaonan Wang},
title = {AI-enhanced multi-scale smart systems for decarbonization in the chemical industry: a pathway to sustainable and efficient production},
year = {2025},
journal = {Technology Review for Carbon Neutrality},
volume = {1},
pages = {9550005},
keywords = {artificial intelligence, decarbonization, digital twins, Chemical industry, multi-scale process system engineering, sustainable production},
url = {https://www.sciopen.com/article/10.26599/TRCN.2025.9550005},
doi = {10.26599/TRCN.2025.9550005},
abstract = {Decarbonizing the energy-intensive chemical industry has emerged as a pivotal challenge in recent years. This article underscores the urgent need for green and smart chemistry and explores decarbonization in chemical sector using a multi-scale smart systems engineering approach. By examining innovations across various scales—from micro-level materials discovery to meso-level process optimization, and up to macro-level chemical industrial park design/redesign—this review illuminates how intelligence approaches can surrogate traditional mechanistic models and thus revolutionize efficiency, sustainability, and carbon neutrality of the chemical industry. Additionally, this review highlights the role of cross-scale modeling in addressing complex challenges in chemical processes through practical applications cases. Further key challenges are identified including data management, model interoperability, and industrial integration, alongside economic, social, and ethical considerations. Finally, it outlines future research directions, emphasizing interdisciplinary approaches to advance the industry toward a greener, more efficient, and carbon-neutral future, aligning with global sustainability objectives.}
}