Machine learning techniques to predict the flame state, temperature and species concentrations in counter-flow diffusion flames operated with CH4/CO/H2-air mixtures

Author:

Prieler Rene,Moser Matthias,Eckart SvenORCID,Krause HartmutORCID,Hochenauer Christoph

Publisher

Elsevier BV

Subject

Organic Chemistry,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering

Reference61 articles.

1. Energy savings in reheating furnaces through process modelling;Zanoni;Procedia Manuf,2020

2. Numerical modelling of chemistry, radiation and transient heating characteristics in high temperature processes;Prieler,2016

3. Experimental study and proposed power correlation for laminar burning velocity of hydrogen-diluted methane with respect to pressure and temperature variation;Eckart;Int J Hydrogen Energy,2021

4. Experimental and numerical investigations on extinction strain rates in non-premixed counterflow methane and propane flames in an oxygen reduced environment;Eckart;Fuel,2021

5. Flame classification through the use of an artificial neural network trained with a genetic algorithm;Gómez,2013

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