Frontiers of Chemical Science and Engineering >
Smart systems engineering contributing to an intelligent carbon-neutral future: opportunities, challenges, and prospects
Received date: 01 Sep 2021
Accepted date: 06 Nov 2021
Published date: 15 Jun 2022
Copyright
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.
Key words: machine learning; modeling; material; industrial applications; environment
Xiaonan Wang , Jie Li , Yingzhe Zheng , Jiali Li . Smart systems engineering contributing to an intelligent carbon-neutral future: opportunities, challenges, and prospects[J]. Frontiers of Chemical Science and Engineering, 2022 , 16(6) : 1023 -1029 . DOI: 10.1007/s11705-022-2142-6
1 |
SuvarnaM, YapK S, YangW, LiJ, NgY T, WangX. Cyber-physical production systems for data-driven, decentralized, and secure manufacturing—a perspective. Engineering, 2021, 7( 9): 1212– 1223
|
2 |
LiL, WangX. Design and operation of hybrid renewable energy systems: current status and future perspectives. Current Opinion in Chemical Engineering, 2021, 31 : 100669
|
3 |
FangH, ZhouJ, WangZ, QiuZ, SunY, LinY, ChenK, ZhouX, PanM. Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations. Frontiers of Chemical Science and Engineering, 2022, 16( 2): 274– 287
|
4 |
CheeE, WongW C, WangX. An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system. Frontiers of Chemical Science and Engineering, 2022, 16( 2): 237– 250
|
5 |
LiJ, LimK, YangH, RenZ, RaghavanS, ChenP, BuonassisiT, WangX. Applications through the whole life cycle of material discovery. Matter, 2020, 3( 2): 393– 432
|
6 |
BertoliniM, MezzogoriD, NeroniM, ZammoriF. Machine learning for industrial applications: a comprehensive literature review. Expert Systems with Applications, 2021, 175 : 114820
|
7 |
GuoH, WuS, TianY, ZhangJ, LiuH. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: a review. Bioresource Technology, 2021, 319 : 124114
|
8 |
InderwildiO, ZhangC, WangX, KraftM. The impact of intelligent cyber-physical systems on the decarbonization of energy. Energy & Environmental Science, 2020, 13( 3): 744– 771
|
9 |
LuS, ZhouQ, OuyangY, GuoY, LiQ, WangJ. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nature Communications, 2018, 9( 1): 1– 8
|
10 |
XuS, LiJ, CaiP, LiuX, LiuB, WangX. Self-improving photosensitizer discovery system via Bayesian search with first-principle simulations. Journal of the American Chemical Society, 2021, 143( 47): 19769– 19777
|
11 |
LiJ, TelychkoM, YinJ, ZhuY, LiG, SongS, YangH, LiJ, WuJ, LuJ, WangX. Machine vision automated chiral molecule detection and classification in molecular imaging. Journal of the American Chemical Society, 2021, 143( 27): 10177– 10188
|
12 |
OviedoF, RenZ, SunS, SettensC, LiuZ, HartonoN T P, RamasamyS, DeCostB L, TianS I P, RomanoG.
|
13 |
SchwallerP, ProbstD, VaucherA C, NairV H, KreutterD, LainoT, ReymondJ L. Mapping the space of chemical reactions using attention-based neural networks. Nature Machine Intelligence, 2021, 3( 2): 144– 152
|
14 |
LiJ, ChenT, LimK, ChenL, KhanS A, XieJ, WangX. Deep learning accelerated gold nanocluster synthesis. Advanced Intelligent Systems, 2019, 1( 3): 1900029
|
15 |
RenZ, Tian S I P, Noh J, OviedoF, XingG, LiangQ, ZhuR, Aberle A, SunS, WangX. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. SSRN, 2021. doi:10.2021.ssrn.3862821
|
16 |
SuvarnaM, BüthL, HejnyJ, MennengaM, LiJ, NgY T, HerrmannC, WangX. Smart manufacturing for smart cities—overview, insights, and future directions. Advanced Intelligent Systems, 2020, 2( 10): 2000043
|
17 |
GajjarS, KulahciM, PalazogluA. Real-time fault detection and diagnosis using sparse principal component analysis. Journal of Process Control, 2018, 67 : 112– 128
|
18 |
TanD, SuvarnaM, Shee TanY, LiJ, WangX. A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing. Applied Energy, 2021, 291 : 116808
|
19 |
WongW, CheeE, LiJ, WangX. Recurrent neural network-based model predictive control for continuous pharmaceutical manufacturing. Mathematics, 2018, 6( 11): 242
|
20 |
EvansR, GaoJ. DeepMind AI reduces google data centre cooling bill by 40%. DeepMind, 2016
|
21 |
JainS, PrestoA A, ZimmermanN. Spatial modeling of daily PM2.5, NO2, and CO concentrations measured by a low-cost sensor network: comparison of linear, machine Learning, and hybrid land use models. Environmental Science & Technology, 2021, 55( 13): 8631– 8641
|
22 |
TuttleJ F, BlackburnL D, AnderssonK, PowellK M. A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling. Applied Energy, 2021, 292 : 116886
|
23 |
HeoS K, NamK J, TariqS, LimJ Y, ParkJ, YooC K. A hybrid machine learning-based multi-objective supervisory control strategy of a full-scale wastewater treatment for cost-effective and sustainable operation under varying influent conditions. Journal of Cleaner Production, 2021, 291 : 125853
|
24 |
YanB, LiangR, LiB, TaoJ, ChenG, ChengZ, ZhuZ, LiX. Fast identification and characterization of residual wastes via laser-induced breakdown spectroscopy and machine learning. Resources, Conservation and Recycling, 2021, 174 : 105851
|
25 |
LiJ, ZhuX, LiY, TongY W, OkY S, WangX. Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: application of machine learning on waste-to-resource. Journal of Cleaner Production, 2021, 278 : 123928
|
26 |
LiJ, PanL, SuvarnaM, WangX. Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening. Chemical Engineering Journal, 2021, 426 : 131285
|
27 |
YuanX, SuvarnaM, LowS, DissanayakeP D, LeeK B, LiJ, WangX, OkY S. Applied machine learning for prediction of CO2 adsorption on biomass waste-derived porous carbons. Environmental Science & Technology, 2021, 55( 17): 11925– 11936
|
/
〈 | 〉 |