Abstract
Combustion performance monitoring is a challenging task due to insufficient post combustion data and insights at critical furnace areas. Post combustion insights are valuable to reflect the plant’s efficiency, reliability, and used in boiler tuning programmes. Boiler tuning, which is scheduled after all the preventive maintenance suggested by boiler manufacturer, could not addressed the operational issues face by plant operators. A system-level digital twin incorporating both computational fluid dynamics (CFD) and machine learning modules is proposed in the current study. The proposed tool could act as a combustion monitoring system to diagnose, pinpoints boiler problems, and troubleshoots to reduce maintenance time and optimize operations. The system recommends operating parameters for different coal types and furnace conditions. The tool can be used as a guideline in daily operation monitoring/optimization and in risk assessments of new coals. The current paper discusses the general architecture of the proposed tool and some of the preliminary results based on the plant’s historical data.
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References
Speight, J.G.: Coal-Fired Power Generation Handbook, 1st edn. Scrivener Publishing LLC, Massachusetts (2013)
Beckmann, A.M., Mancini, M., Weber, R., Seebold, S., Muller, M.: Measurements and CFD modelling of a pulverized coal flame with emphasis on ash deposition. Fuel 167, 168–179 (2016)
Mat Zaid, M.Z.S., Wahid, M.A., Mailah, M., Mazlan, M.A.: Coal combustion analysis tool in coal fired power plant for slagging and fouling guidelines. In: Editor, F., Editor, S. (eds.) The 10th International Meeting of Advances in Thermofluids 2018, vol. 2062, AIP Conference Proceedings (2019)
Zhou, W.: Coal fired boiler flow characterization, combustion optimization and Efficiency improvement guided by computational fluid dynamics (CFD) modeling. Research Gate (2017).
Achieving Better Coal Plant Efficiency and Emissions Control with Digital, GE (2017)
Laubscher, R., Rousseau, P.: Coupled simulation and validation of a utility-scale pulverized coal-fired boiler radiant final-stage superheater. Thermal Sci. Eng. Progress 18, 100512 (2020)
Belosevic, S., Tomanovic, I., Crnomarkovic, N., Milicevic, A.: Full-scale CFD investigation of gas-particle flow, interactions and combustion in tangentially fired pulverized coal furnace. Energy 179, 1036–1053 (2019)
Rousseau, P., Laubscher, R.: Analysis of the impact of coal quality on the heat transfer distribution in a high-ash pulverized coal boiler using co-simulation. Energy 198, 117343 (2020)
Rowan, S.L., Celik, I., Gutierrez, A.D., Vargas, J.E.: A reduced order model for the design of oxy-coal combustion systems. J. Combustion 2015, 1–9 (2015)
Zhao, Y.: Optimization of thermal efficiency and unburned carbon in fly ash of coal-fired utility boiler via grey wolf optimizer algorithm. IEEE Access 7, 114414–114425 (2019)
Sangram, B.S., Jagannath, L.M.: Modeling and optimizing boiler design using neural network and firefly algorithm. J. Intell. Syst. 27, 393–412 (2018)
Yang, J.-H., Kim, J.-E.A., Hong, J., Kim, M., Ryu, C., Kim, Y.J., Park, H.Y., Baek, S.H.: Effects of detailed operating parameters on combustion in two 500-MWe coal-fired boilers of an identical design. Fuel 144, 145–156 (2015)
Czajka, K.M., Modlinski, N., Kisiela-Czajka, A.M., Naidoo, R., Peta, S., Nyangwa, B.: Volatile matter release from coal at different heating rates – experimental study and kinetic modelling. J. Anal. Appl. Pyrol. 139, 282–290 (2019)
Yeoh, G.H., Yuen, K.K.: Computational Fluid Dynamics in Fire Engineering: Theory, Modelling and Practice, 1st edn. Butterworth-Heinemann, USA (2009)
Joseph, D., Benedicte, C.: Discrete Ordinates and Monte Carlo Methods for Radiative Transfer Simulation applied to CFD combustion modelling. Research Gate (2009)
Liu, H.: Encyclopedia of Machine Learning. Springer, Boston (2010)
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Rahman, M.N., Noor, N.A.W.B.M., Zulkifli, A.Z.S.b., Aris, M.S. (2021). Development of a Fast Response Combustion Performance Monitoring, Prediction, and Optimization Tool for Power Plants. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_105
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DOI: https://doi.org/10.1007/978-3-030-68154-8_105
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