Fuel and Combustion

Fuel and Combustion

Experimental Study and Optimization of the Equivalence Ratio in Gas Burners Using Genetic Algorithm for Performance Enhancement and Emission Reduction

Document Type : Original Article

Authors
Amin Jodat 1
Karim Jafarian 2
Mojtaba Najafian 3
Saeed Shadmir 4
1 Department of Mechanical Engineering, University of Bojnord, 9453155111 Bojnord, North Khorasan, Iran
2 Department of Mechanical Engineering, University of Bojnord, Bojnord, Iran
3 Research Institute, Applied Science University of North Khorasan, Bojnord, Iran
4 3-Department of Mechanical Engineering, University of Bojnord, Bojnord, Iran
10.22034/jfnc.2025.539531.1435
Abstract
This study presents an experimental investigation aimed at improving the thermal performance and reducing pollutant emissions of industrial gas burners. Tests were conducted on a steel boiler with a thermal capacity of 100,000 kcal/h, focusing on key parameters such as flame temperature, combustion efficiency, and concentrations of NOₓ and CO across a range of equivalence ratios. Each experiment was repeated and analyzed using the expanded uncertainty method to ensure reliable results. To evaluate multiple performance criteria simultaneously, a genetic algorithm coupled with data normalization techniques was employed. The results showed that increasing the equivalence ratio up to φ = 0.83 leads to noticeable improvements in both flame temperature and efficiency, after which both begin to decline. The highest NOₓ levels were observed near this ratio but remained within acceptable environmental limits. The lowest CO concentrations occurred in the range φ = 0.80–0.85; however, in some cases, CO exceeded regulatory thresholds and was identified as the limiting factor in determining the optimal equivalence ratio. These findings suggest that fine-tuning the air-to-fuel ratio using intelligent optimization algorithms such as genetic algorithms, can serve as an effective and practical approach to achieve a stable, efficient, and low-emission combustion in industrial gas burners. This method can be readily applied in various sectors including thermal furnaces, power plants, baking processes, and the food industry.
 
Keywords
Gas burners
Genetic algorithm
Equivalence ratio
Efficiency
Experimental setup

Subjects

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