Next Article in Journal
Existence of Solutions for Anti-Periodic Fractional Differential Inclusions Involving ψ-Riesz-Caputo Fractional Derivative
Next Article in Special Issue
A Comparative Study of Bitcoin Price Prediction Using Deep Learning
Previous Article in Journal
Cantor Paradoxes, Possible Worlds and Set Theory
Article

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

1
Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran
2
Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran 1417853933, Iran
3
Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
4
School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, India
5
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
6
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Mathematics 2019, 7(7), 629; https://doi.org/10.3390/math7070629
Received: 6 June 2019 / Revised: 24 June 2019 / Accepted: 3 July 2019 / Published: 15 July 2019
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). View Full-Text
Keywords: modeling; optimization; steam boiler; neural network; response-surface modeling; optimization; steam boiler; neural network; response-surface
Show Figures

Figure 1

MDPI and ACS Style

Maddah, H.; Sadeghzadeh, M.; Ahmadi, M.H.; Kumar, R.; Shamshirband, S. Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM). Mathematics 2019, 7, 629. https://doi.org/10.3390/math7070629

AMA Style

Maddah H, Sadeghzadeh M, Ahmadi MH, Kumar R, Shamshirband S. Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM). Mathematics. 2019; 7(7):629. https://doi.org/10.3390/math7070629

Chicago/Turabian Style

Maddah, Heydar, Milad Sadeghzadeh, Mohammad H. Ahmadi, Ravinder Kumar, and Shahaboddin Shamshirband. 2019. "Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)" Mathematics 7, no. 7: 629. https://doi.org/10.3390/math7070629

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop