In this paper we present a novel approach to the problem of three-way catalytic converter dynamic modeling; one of the main issues related to modeling these devices is the reaction kinetics submodel, that has to be at the same time simple and flexible enough to capture all the significant features of the real system. We propose the use of machine learning techniques to solve this problem: a neural network structure for the kinetic submodel and a genetic algorithm to tune its parameters. In this way the difficulties arising from the identification of the resulting overall model are avoided.

Three-way catalytic converter modelling: A machine learning approach for the reaction kinetics

Glielmo L;
1999

Abstract

In this paper we present a novel approach to the problem of three-way catalytic converter dynamic modeling; one of the main issues related to modeling these devices is the reaction kinetics submodel, that has to be at the same time simple and flexible enough to capture all the significant features of the real system. We propose the use of machine learning techniques to solve this problem: a neural network structure for the kinetic submodel and a genetic algorithm to tune its parameters. In this way the difficulties arising from the identification of the resulting overall model are avoided.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/10659
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