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Title

A single-step time-integrator of a methane-air chemical system using artificial neural networks

AuthorsBlasco, J.; Fueyo, Norberto; Larroya, J. C.; Dopazo, C.; Chen, Y.-J.
Issue Date1999
PublisherPergamon Press
CitationComputers and Chemical Engineering 23: 1127-1133 (1999)
AbstractThe present paper reports a novel method for embedding a reduced chemical system, suitable for the simulation of methane-air combustion, in an artificial neural network (ANN). The use of ANNs as a means of storing in a compact manner the chemical kinetics of a system is an emerging alternative to other methods, the full potential of which remains to be exploited. The current contribution introduces two novelties: firstly, the compositional domain is split into subdomains, for each of which an ANN fitting is attempted; and secondly, the timestep is introduced as an additional input to the network, thus increasing the accuracy and speed of the method. The paper introduces three alternative types of network, and describes in detail the methodology used for their construction and validation, as well as the validation results. The level of accuracy attained is at least one order of magnitude better than with previously-published ANN approaches. | The present paper reports a novel method for embedding a reduced chemical system, suitable for the simulation of methane-air combustion, in an artificial neural network (ANN). The use of ANNs as a means of storing in a compact manner the chemical kinetics of a system is an emerging alternative to other methods, the full potential of which remains to be exploited. The current contribution introduces two novelties: firstly, the compositional domain is split into subdomains, for each of which an ANN fitting is attempted; and secondly, the timestep is introduced as an additional input to the network, thus increasing the accuracy and speed of the method. The paper introduces three alternative types of network, and describes in detail the methodology used for their construction and validation, as well as the validation results. The level of accuracy attained is at least one order of magnitude better than with previously-published ANN approaches.
URIhttp://hdl.handle.net/10261/51196
DOI10.1016/S0098-1354(99)00278-1
Identifiersdoi: 10.1016/S0098-1354(99)00278-1
issn: 0098-1354
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