IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v35y2010i8p3501-3507.html
   My bibliography  Save this article

A method for estimation of recoverable heat from blowdown systems during steam generation

Author

Listed:
  • Bahadori, Alireza
  • Vuthaluru, Hari B.

Abstract

During the generation of steam, most water impurities are not evaporated with the steam and thus concentrate in the boiler water. The concentration of the impurities is usually regulated by the adjustment of the continuous blowdown valve, which controls the amount of water (and concentrated impurities) purged from the steam drum. Since a certain amount of continuous blowdown must be maintained for satisfactory boiler performance, a significant quantity of heat is removed from the boiler. It is necessary to provide a simple-to-use method to calculate the total amount of heat that is recoverable using this system. In the present work, a simple-to-use predictive tool, which is easier than existing approaches, less complicated with fewer computations and minimize the complex and time-consuming calculation steps, is formulated to arrive at an appropriate estimation of the percent of blowdown that is flashed to steam as a function of flash drum pressure and operating boiler drum pressure followed by the calculation of the amount of heat recoverable from the condensate. Since all of the heat in the flashed steam is recoverable, the total percent of heat recoverable from the flash tank and heat-exchanger system is calculated in the final step. Results show that the proposed predictive tool has a very good agreement with the reported data wherein the average absolute deviation percent was observed to be around 1.47%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:8:p:3501-3507
    DOI: 10.1016/j.energy.2010.04.054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544210002628
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2010.04.054?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. Bahadori, Alireza & Vuthaluru, Hari B., 2010. "A simple method for the estimation of thermal insulation thickness," Applied Energy, Elsevier, vol. 87(2), pages 613-619, February.
    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. Bujak, Janusz, 2009. "Optimal control of energy losses in multi-boiler steam systems," Energy, Elsevier, vol. 34(9), pages 1260-1270.
    4. Bahadori, Alireza & Vuthaluru, Hari B., 2010. "Simple equations to correlate theoretical stages and operating reflux in fractionators," Energy, Elsevier, vol. 35(3), pages 1439-1446.
    5. 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.
    6. Domenichini, R. & Gallio, M. & Lazzaretto, A., 2010. "Combined production of hydrogen and power from heavy oil gasification: Pinch analysis, thermodynamic and economic evaluations," Energy, Elsevier, vol. 35(5), pages 2184-2193.
    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)

    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. Bujak, Janusz, 2009. "Optimal control of energy losses in multi-boiler steam systems," Energy, Elsevier, vol. 34(9), pages 1260-1270.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Gross error isolability for operational data in power plants," Energy, Elsevier, vol. 74(C), pages 918-927.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    12. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    13. 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.
    14. 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.
    15. Fast, M. & Assadi, M. & De, S., 2009. "Development and multi-utility of an ANN model for an industrial gas turbine," Applied Energy, Elsevier, vol. 86(1), pages 9-17, January.
    16. Liszka, Marcin & Malik, Tomasz & Manfrida, Giampaolo, 2012. "Energy and exergy analysis of hydrogen-oriented coal gasification with CO2 capture," Energy, Elsevier, vol. 45(1), pages 142-150.
    17. Suresh, M.V.J.J. & Reddy, K.S. & Kolar, Ajit Kumar, 2011. "ANN-GA based optimization of a high ash coal-fired supercritical power plant," Applied Energy, Elsevier, vol. 88(12), pages 4867-4873.
    18. Esmaeil Jadidi & Mohammad Hasan Khoshgoftar Manesh & Mostafa Delpisheh & Viviani Caroline Onishi, 2021. "Advanced Exergy, Exergoeconomic, and Exergoenvironmental Analyses of Integrated Solar-Assisted Gasification Cycle for Producing Power and Steam from Heavy Refinery Fuels," Energies, MDPI, vol. 14(24), pages 1-29, December.
    19. 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.
    20. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.

    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:35:y:2010:i:8:p:3501-3507. 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.