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

Heydar Maddah; Milad Sadeghzadeh; Mohammad Hossein Ahmadi; Ravinder Kumar; Shahab Shamshirband

doi:10.20944/preprints201906.0057.v1

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preprints.org > engineering > mechanical engineering > doi: 10.20944/preprints201906.0057.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Heydar Maddah

Milad Sadeghzadeh

Mohammad Hossein Ahmadi

Ravinder Kumar

Shahab Shamshirband

Version 1 : Received: 6 June 2019 / Approved: 7 June 2019 / Online: 7 June 2019 (12:25:29 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

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: the dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, 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).

Keywords

Modeling; Optimization; Steam Boiler; Neural Network; Response-Surface

Subject

Engineering, Mechanical Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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