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Title: Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques

Abstract

A new approach for the detection of real-time properties of flames is used in this project to develop improved diagnostics and controls for natural gas fired furnaces. The system utilizes video images along with advanced image analysis and artificial intelligence techniques to provide virtual sensors in a stand-alone expert shell environment. One of the sensors is a flame sensor encompassing a flame detector and a flame analyzer to provide combustion status. The flame detector can identify any burner that has not fired in a multi-burner furnace. Another sensor is a 3-D temperature profiler. One important aspect of combustion control is product quality. The 3-D temperature profiler of this on-line system is intended to provide a tool for a better temperature control in a furnace to improve product quality. In summary, this on-line diagnostic and control system offers great potential for improving furnace thermal efficiency, lowering NOx and carbon monoxide emissions, and improving product quality. The system is applicable in natural gas-fired furnaces in the glass industry and reheating furnaces used in steel and forging industries.

Authors:
Publication Date:
Research Org.:
University of Missouri-Columbia
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
862201
Report Number(s):
DOE/CH/11032
TRN: US200712%%68
DOE Contract Number:  
FC36-00CH11032
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
03 NATURAL GAS; 36 MATERIALS SCIENCE; ARTIFICIAL INTELLIGENCE; BURNERS; CARBON MONOXIDE; COMBUSTION; COMBUSTION CONTROL; CONTROL SYSTEMS; DETECTION; FLAMES; FORGING; FURNACES; GLASS INDUSTRY; NATURAL GAS; ON-LINE SYSTEMS; STEELS; TEMPERATURE CONTROL; THERMAL EFFICIENCY

Citation Formats

Shahla Keyvan. Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques. United States: N. p., 2005. Web. doi:10.2172/862201.
Shahla Keyvan. Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques. United States. doi:10.2172/862201.
Shahla Keyvan. Thu . "Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques". United States. doi:10.2172/862201. https://www.osti.gov/servlets/purl/862201.
@article{osti_862201,
title = {Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques},
author = {Shahla Keyvan},
abstractNote = {A new approach for the detection of real-time properties of flames is used in this project to develop improved diagnostics and controls for natural gas fired furnaces. The system utilizes video images along with advanced image analysis and artificial intelligence techniques to provide virtual sensors in a stand-alone expert shell environment. One of the sensors is a flame sensor encompassing a flame detector and a flame analyzer to provide combustion status. The flame detector can identify any burner that has not fired in a multi-burner furnace. Another sensor is a 3-D temperature profiler. One important aspect of combustion control is product quality. The 3-D temperature profiler of this on-line system is intended to provide a tool for a better temperature control in a furnace to improve product quality. In summary, this on-line diagnostic and control system offers great potential for improving furnace thermal efficiency, lowering NOx and carbon monoxide emissions, and improving product quality. The system is applicable in natural gas-fired furnaces in the glass industry and reheating furnaces used in steel and forging industries.},
doi = {10.2172/862201},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Dec 01 00:00:00 EST 2005},
month = {Thu Dec 01 00:00:00 EST 2005}
}

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