Smart systems engineering contributing to an intelligent carbon-neutral future: opportunities, challenges, and prospects

doi:10.1007/s11705-022-2142-6

Front. Chem. Sci. Eng.

2022, Vol. 16

Issue (6) : 1023-1029 https://doi.org/10.1007/s11705-022-2142-6

VIEWS & COMMENTS

Smart systems engineering contributing to an intelligent carbon-neutral future: opportunities, challenges, and prospects

Xiaonan Wang¹(

), Jie Li², Yingzhe Zheng², Jiali Li²

¹. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
². Department of Chemical & Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore 117585, Singapore

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Abstract

This communication paper provides an overview of multi-scale smart systems engineering (SSE) approaches and their applications in crucial domains including materials discovery, intelligent manufacturing, and environmental management. A major focus of this interdisciplinary field is on the design, operation and management of multi-scale systems with enhanced economic and environmental performance. The emergence of big data analytics, internet of things, machine learning, and general artificial intelligence could revolutionize next-generation research, industry and society. A detailed discussion is provided herein on opportunities, challenges, and future directions of SSE in response to the pressing carbon-neutrality targets.

Keywords machine learning modeling material industrial applications environment

Corresponding Author(s): Xiaonan Wang

Online First Date: 28 March 2022 Issue Date: 28 June 2022

Cite this article:

Xiaonan Wang,Jie Li,Yingzhe Zheng, et al. Smart systems engineering contributing to an intelligent carbon-neutral future: opportunities, challenges, and prospects[J]. Front. Chem. Sci. Eng., 2022, 16(6): 1023-1029.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-022-2142-6
https://academic.hep.com.cn/fcse/EN/Y2022/V16/I6/1023

Fig.1 Illustration of multi-scale smart systems engineering (SSE). Multiple length and time scales ranging from atoms, molecules, and particles at nano/microscale towards process at mesoscale, till macroscale global environment are investigated using the general SSE approaches.

Fig.2 Typical machine learning methods and their application in material discovery.

Fig.3 Schematic overview of intelligent manufacturing.

Fig.4 Smart systems approach with machine learning (ML) application in environmental management.

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