Obtaining a reduced kinetic mechanism for methyl decanoate using layerless neural networks

• Published in: Fuel (Guildford) Vol. 255; p. 115787
• Main Authors: Pereira, F.N., De Bortoli, A.L.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-11-2019; Elsevier BV
• Subjects: Asymptotic analysis; Biodiesel; Biodiesel fuels; Biofuels; Directed relation graph; Layerless neural network; Mechanism reduction; Methyl decanoate; Neural networks; Organic chemistry; Reduction; Strategy; Layerless neural network; Directed relation graph; Asymptotic analysis; Mechanism reduction; Biodiesel; Methyl decanoate
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2019.115787

•Solutions are presented for turbulent jet diffusion flames of MD.•LNNs are used to obtain reduced kinetic mechanisms for MD.•Analytical-numerical solutions help to understand the combustion of MD.•There are environmental benefits related to the use of methyl decanoate.
Major efforts in the search for techniques for the development of reduced kinetic mechanisms for biodiesel have been observed, since these mechanisms may have thousands of species. This paper proposes a reduction strategy and presents the development of a reduced kinetic mechanism for piloted jet diffusion flame of methyl decanoate (MD). The strategy consists of applying the DRG, Directed Relation Graph, technique for initial reduction, and the use of Layerless Neural Network (LNN) to define the main chain and obtain a skeletal mechanism. Hence the hypotheses of steady-state and partial equilibrium are applied, and the assumptions are justified by an asymptotic analysis. The main advantage of the strategy is to reduce the work required to solve the system of chemical equations by at least two orders of magnitude for MD, since the number of species is decreased in the same order.