IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v34y2009i9p1260-1270.html
   My bibliography  Save this article

Optimal control of energy losses in multi-boiler steam systems

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544209001789
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2009.05.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    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. Bogdan, Željko & Cehil, Mislav & Kopjar, Damir, 2007. "Power system optimization," Energy, Elsevier, vol. 32(6), pages 955-960.
    6. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    7. Bujak, J., 2008. "Mathematical modelling of a steam boiler room to research thermal efficiency," Energy, Elsevier, vol. 33(12), pages 1779-1787.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Gross error isolability for operational data in power plants," Energy, Elsevier, vol. 74(C), pages 918-927.
    3. Costanza, Vicente & Rivadeneira, Pablo S., 2015. "Optimal supervisory control of steam generators operating in parallel," Energy, Elsevier, vol. 93(P2), pages 1819-1831.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. Tang, Wei & Feng, Huijun & Chen, Lingen & Xie, Zhuojun & Shi, Junchao, 2021. "Constructal design for a boiler economizer," Energy, Elsevier, vol. 223(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    3. Nikpey, H. & Assadi, M. & Breuhaus, P., 2013. "Development of an optimized artificial neural network model for combined heat and power micro gas turbines," Applied Energy, Elsevier, vol. 108(C), pages 137-148.
    4. 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.
    5. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    6. 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.
    7. Keçebaş, Ali & Alkan, Mehmet Ali & Yabanova, İsmail & Yumurtacı, Mehmet, 2013. "Energetic and economic evaluations of geothermal district heating systems by using ANN," Energy Policy, Elsevier, vol. 56(C), pages 558-567.
    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. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    10. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Ahmad Hassan Kamal & Muhammad Haider Khan & Awais Ahmad Khan & Hassan Afroze Ahmad & Fahad Ahmed & Noman Hafeez & Rana Muhammad Zawar Sami & Syed Muhammad , 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation," Energies, MDPI, vol. 13(21), pages 1-22, October.
    11. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    12. Kruczek, Tadeusz, 2013. "Determination of annual heat losses from heat and steam pipeline networks and economic analysis of their thermomodernisation," Energy, Elsevier, vol. 62(C), pages 120-131.
    13. Mehleri, E.D. & Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C., 2010. "A new neural network model for evaluating the performance of various hourly slope irradiation models: Implementation for the region of Athens," Renewable Energy, Elsevier, vol. 35(7), pages 1357-1362.
    14. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    15. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    16. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    17. Jorge E. De León-Ruiz & Ignacio Carvajal-Mariscal & Antonin Ponsich, 2019. "Feasibility Analysis and Performance Evaluation and Optimization of a DXSAHP Water Heater Based on the Thermal Capacity of the System: A Case Study," Energies, MDPI, vol. 12(20), pages 1-38, October.
    18. Barelli, L. & Bidini, G. & Bonucci, F., 2009. "Development of the regulation mapping of 1Â MW internal combustion engine for diagnostic scopes," Applied Energy, Elsevier, vol. 86(7-8), pages 1087-1104, July.
    19. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    20. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:34:y:2009:i:9:p:1260-1270. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.