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

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.

Graphical abstract

Keywords

machine learning / modeling / material / industrial applications / environment

References

[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. . Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks. npj Computational Materials, 2019, 5( 1): 1– 9

[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 PM_2.5, 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

Acknowledgements

This work is supported by Tsinghua University Initiative Scientific Research Program and Tsinghua-Foshan Innovation Special Fund (TFISF). Xiaonan Wang thanks the award of Future Chemical Engineers and the Global Chinese Chemical Engineering Symposium (GCCES).

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2022 Higher Education Press 2022