The optimization of Arrhenius parameters for methane two-step reaction using machinelearning
Publish place: The 31st annual conference between Iran Mechanical Engineering and the 9th Iran Power Plant Industry Conference
Publish Year: 1402
Type: Conference paper
Language: English
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ISME31_363
Index date: 31 May 2023
The optimization of Arrhenius parameters for methane two-step reaction using machinelearning abstract
The current concerns about the level of emission ofpollutants and their effects on the environmenthave made it important to predict the flow,temperature, concentration of species andemissions of various combustion systems for thedesign and improvement of combustion equipment.Experimental investigation of combustion isexpensive, and numerical simulation has been usedfor decades and has become a powerful tool forsimulating complex combustion processes. One ofthe common methods in combustion simulation isto use the Arrhenius equation. It should be notedthat the coefficients extracted from the Arrheniusequation can only be used for a certain range. If theequation with those coefficients is used in a rangeoutside the defined range, it will lead to a largeinaccuracy. Therefore, in order to reduce theseinaccuracies, the optimization of these coefficientshas been used using the methods of least squares,genetic algorithm and PSO algorithm according tothe two-step experimental data.
The optimization of Arrhenius parameters for methane two-step reaction using machinelearning Keywords:
The optimization of Arrhenius parameters for methane two-step reaction using machinelearning authors
Alireza Eshaghi
Mechanical Engineering Department, Iran University of Science and Technology, Tehran;
Zeinab Pouransari
Mechanical Engineering Department, Iran University of Science and Technology, Tehran;