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Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)

Author

Listed:
  • Heydar Maddah

    (Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran)

  • Milad Sadeghzadeh

    (Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran 1417853933, Iran)

  • Mohammad Hossein Ahmadi

    (Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran)

  • Ravinder Kumar

    (School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, India)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

Abstract

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: Dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, and radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM).

Suggested Citation

  • Heydar Maddah & Milad Sadeghzadeh & Mohammad Hossein Ahmadi & Ravinder Kumar & Shahaboddin Shamshirband, 2019. "Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)," Mathematics, MDPI, vol. 7(7), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:7:p:629-:d:248633
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    References listed on IDEAS

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    Cited by:

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    2. Tingyi Chai & Chang Liu & Yichuan Xu & Mengru Ding & Muyao Li & Hanyu Yang & Xun Dou, 2024. "Optimal Dispatching Strategy for Textile-Based Virtual Power Plants Participating in GridLoad Interactions Driven by Energy Price," Energies, MDPI, vol. 17(20), pages 1-20, October.

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