[1] Pantelides C.C., Renfro J.G., The online use of first-principles models in process operations: Review, current status and future needs. Computers & Chemical Engineering, 2013, 51: 136–148.
[2] Oppelt M., Graube M., Barth M., et al., Enabling the integrated use of simulation within the life cycle of a process plant: an initial roadmap Results of an in-depth online study. 13th IEEE International Conference on Industrial Informatics, Cambridge, UK, 2015, 7: 22–24.
DOI: 10.1109/INDIN.2015.7281709.
[3] Seki T., Fukano G., Kawaguchi K., et al., Innovative plant operations by using tracking simulator. Annual Conference of the SICE, Chofu, Japan, 2008, 8: 20–22. DOI: 10.1109/SICE.2008.4655008.
[4] Wang C.B., Duan Q.Z., Zhang C., et al., Applications of online simulation supporting PWR operations. Nuclear Engineering and Technology, 2021, 53(3): 842–850.
[5] Martinez G.S., Karhela T., Vyatkin V., et al., An OPC UA based architecture for testing tracking simulation methods. 13th IEEE International Symposium on Parallel and Distributed Processing with Applications, Helsinki, Finland, 2015, 8: 275–280.
DOI: 10.1109/Trustcom.2015.644.
[6] Martinez G.S., Karhela T., Niemisto H., et al., A hybrid approach for the initialization of tr-acking simulation systems. 20th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Luxembourg, Luxembourg, 2015, 9: 8–11.
DOI: 10.1109/ETFA.2015.7301532.
[7] Martinez G.S., Karhela T.A., Ruusu R.J., et al., An integrated implementation methodology of a lifecycle-wide tracking simulation architecture. IEEE Access, 2018, 6: 15391–15407.
[8] Nakaya M., Seki T., Kawaguchi K., et al., Model parameter estimation by tracking simulator for the innovation of plant operation. IFAC Proceedings Volumes, 2008, 41(2): 2168–2173.
[9] Nakaya M., Fukano G., Onoe Y., et al., On-line simulator for plant operation. 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006, 6: 21–23.
DOI: 10.1109/WCICA.2006.1713505.
[10] Martinez G.S., Miettinen T., Aikalaa, et al., Parameters selection in predictive online simulation. 14th IEEE International Conference on Industrial Informatics, Poitiers, France, 2016, 6: 19–21.
DOI: 10.1109/INDIN.2016.7819254.
[11] Nakaya M., Li X.C., On-line tracking simulator with a hybrid of physical and Just-In-Time models. Journal of Process Control, 2013, 23(2): 171–178.
[12] Kawaguchi K., Onoe Y., Nakaya M., et al., An application of on-line tracking simulator to a PEMFC. SICE-ICASE International Joint Conference, Busan, South Korea, 2006, 10: 18–21.
DOI: 10.1109/INDIN.2016.7819254.
[13] Ruusu R., Martinez G.S., Karhela T., et al., Sliding mode SISO control of model parameters for implicit dynamic feedback estimation of industrial tracking simulation systems. 43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON), Beijing, China, 2017, 10: 6927–6932.
DOI: 10.1109/IECON.2017.8217211.
[14] Shtesssel Y., Edwards C., Fridman L., et al., Sliding mode control and observation. Springer New York, 2014.
[15] Sedaghat M., Taheri B., Farhadi P., Power point tracking in photovoltaic systems by sliding mode control. 10th International Symposium on Advanced Topics in Electrical Engineering, 2017, 3: 23–25.
DOI: 10.1109/ATEE.2017.7905097.
[16] Lu R.N., IOP, Research on the structure of smart power plant based on in-depth learning. 5th International Conference on Environmental Science and Material Application (ESMA), Xi’an, China, 2019, 12: 15–16.
DOI: 10.1088/1755-1315/440/3/032113.
[17] Vagnoni E., Gerini F., Cherkaoui R., et al., Digitalization in hydropower generation: development and numerical validation of a model-based smart power plant supervisor. 30th IAHR Symposium on Hydraulic Machinery and Systems (IAHR), Electr Network, 2021, 3: 21–26.
DOI: 10.1088/1755-1315/774/1/012107.
[18] Snes A., Willersrud A., Kretz F., et al., Predictive maintenance and life cycle estimation for hydro power plants with real-time analytics. Hydro 2018, Gdansk, Poland, 2018.
[19] Liu Z.F., Zhang Y.Z., Yang C.B., et al., Generalized distributed four-domain digital twin system for intelligent manufacturing. Journal of Central South University, 2022, 29(1): 209–225.
[20] Xu J., Huang E., Hsieh L., et al., Simulation optimization in the era of Industrial 4.0 and the Industrial Internet. Journal of Simulation, 2016, 10(4): 310–320.
[21] Yang C., Zhang Z.L., Wu H.C., et al., Dynamic characteristics analysis of a 660 MW ultra-supercritical circulating fluidized bed boiler. Energies, 2022, 15(11): 20.
[22] Zhang Z.L., Yang C., Wu H.C., et al., Modeling and simulation of the start-up process of a 660 MW ultra-supercritical circulating fluidized bed boiler. Computers & Chemical Engineering, 2023, 169: 108079.
[23] Li S.P., Sheen J., Jang S.J., et al., Modified lumped-parameter method for measurements of dielectric susceptibility in ferroelectrics. Japanese Journal of Applied Physics Part 1-Regular Papers Short Notes & Review Papers, 1994, 33(6A): 3617–3621.
[24] Bermudez A., Pena F., Galerkin lumped parameter methods for transient problems. International Journal for Numerical Methods in Engineering, 2011, 87(10): 943–961.
[25] Su J., Improved lumped models for transient radiative cooling of a spherical body. International Communications in Heat and Mass Transfer, 2004, 31(1): 85–94.
[26] Li N., Yan W.P., Structural analysis of 1000 MW ultra supercritical boiler’s steam-water separator. 2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012), Taiyuan, China, 2012, 8: 16–18.
DOI: 10.4028/www.scientific.net/AMM.220223.867.
[27] Wang H.B., Zhou B., Fang S.S., Sliding mode control of permanent magnet synchronous motor speed control system. Transaction of China Electrotechnical Society, 2009, 24(9): 71–77. (in Chinese)