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Optimal control of energy losses in multi-boiler steam systems

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  • Bujak, Janusz

Abstract

This paper describes the development of a mathematical model to determine the optimized energy losses for a set of boilers (with a wide operating margin) supplying a common load. The model can be applied to steam systems that have a group of liquid- or gas-fired shell boilers available for use. The study shows that when the model is applied, the total energy losses of a few boilers working in unison are lower than when a traditional cascade system is used. The differences in energy loss can even reach approximately 12%. The model shows that increasing the heat load from 0 to 30% yields increasing differences in the energy losses between the standard (traditional) and optimized conditions, up to a maximum value of 79kJ/s. As the steam demand grows from 30 to 100%, the total difference in energy losses between the standard and optimized conditions decreases systematically. When the multi-boiler system operates at full thermal power (100%), there are no differences in the energy losses. The greatest energy loss differences occur in the heat load range from 10 to 80%. There will be a reduction in the primary fuel used by about 40,300Nm3 per year if the model is applied. The optimization system can be put into operation in existing and proposed plants. The payback period on investment for the optimization controller is less than half a year.

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  • Bujak, Janusz, 2009. "Optimal control of energy losses in multi-boiler steam systems," Energy, Elsevier, vol. 34(9), pages 1260-1270.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:9:p:1260-1270
    DOI: 10.1016/j.energy.2009.05.005
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    1. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    2. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    3. Savulescu, Luciana Elena & Alva-Argaez, Alberto, 2008. "Direct heat transfer considerations for improving energy efficiency in pulp and paper Kraft mills," Energy, Elsevier, vol. 33(10), pages 1562-1571.
    4. De, S. & Kaiadi, M. & Fast, M. & Assadi, M., 2007. "Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden," Energy, Elsevier, vol. 32(11), pages 2099-2109.
    5. Bujak, J., 2008. "Mathematical modelling of a steam boiler room to research thermal efficiency," Energy, Elsevier, vol. 33(12), pages 1779-1787.
    6. Bogdan, Željko & Cehil, Mislav & Kopjar, Damir, 2007. "Power system optimization," Energy, Elsevier, vol. 32(6), pages 955-960.
    7. Bujak, J., 2008. "Energy savings and heat efficiency in the paper industry: A case study of a corrugated board machine," Energy, Elsevier, vol. 33(11), pages 1597-1608.
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    Cited by:

    1. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    2. Chen, Bin & Ye, Xiao & Shen, Jun & Wang, Sha & Deng, Shengxiang & Yang, Jinbiao, 2021. "Investigations on the energy efficiency limits for industrial boiler operation and technical requirements—taking China’s Hunan province as an example," Energy, Elsevier, vol. 220(C).
    3. Bahadori, Alireza & Vuthaluru, Hari B., 2010. "A method for estimation of recoverable heat from blowdown systems during steam generation," Energy, Elsevier, vol. 35(8), pages 3501-3507.
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    5. Tang, Wei & Feng, Huijun & Chen, Lingen & Xie, Zhuojun & Shi, Junchao, 2021. "Constructal design for a boiler economizer," Energy, Elsevier, vol. 223(C).
    6. Costanza, Vicente & Rivadeneira, Pablo S., 2015. "Optimal supervisory control of steam generators operating in parallel," Energy, Elsevier, vol. 93(P2), pages 1819-1831.
    7. Yu, Byeonghun & Kum, Sung-Min & Lee, Chang-Eon & Lee, Seungro, 2013. "Combustion characteristics and thermal efficiency for premixed porous-media types of burners," Energy, Elsevier, vol. 53(C), pages 343-350.
    8. Barma, M.C. & Saidur, R. & Rahman, S.M.A. & Allouhi, A. & Akash, B.A. & Sait, Sadiq M., 2017. "A review on boilers energy use, energy savings, and emissions reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 970-983.
    9. Lee, Seungro & Kum, Sung-Min & Lee, Chang-Eon, 2011. "An experimental study of a cylindrical multi-hole premixed burner for the development of a condensing gas boiler," Energy, Elsevier, vol. 36(7), pages 4150-4157.
    10. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.

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