In this work, a methodology for building and calibrating the kinetic scheme for the 1D CFD model of a zone-coated automotive Diesel Oxidation Catalyst (DOC) by means of a Genetic Algorithm (GA) approach is presented. The methodology consists of a preliminary experimental activity followed by a modelling, optimization and validation process. The tested aftertreatment component presents zone coating, with the front brick side covered with Zeolites in order to ensure hydrocarbons trapping at low temperature, and Platinum Group Metal (PGM), while the rear brick side presents an alumina washcoat with a different PGM loading. Reactor scale samples representative of each coating zone were tested on a Synthetic Gas Bench (SGB), to fully characterize the component’s behavior in terms of Light-off and hydrocarbons (HC) storage for a wide range of inlet feed compositions and temperatures, representative of engine-out conditions. On the modeling side, a 1D-CFD model of the component was built in GT-SUITE environment and a global kinetic scheme was defined, based on the available literature, expressed in the Arrhenius form. A Genetic Algorithm optimization tool was then used to calibrate reaction rate parameters and active sites densities, by means of a sequential calibration strategy, categorizing the reaction model into several steps according to the experimental test protocol. In each step of the calibration, the number of independent variables was reduced as much as possible and the reactions could be isolated using primary single species tests, moving then to more complex gas mixtures to calibrate the mutual interaction of different species. The model was finally validated over experimental data, showing satisfactory predictive capabilities in terms of both light-off temperatures and oxidation rates, capturing the differences between different coating types as well. The presented methodology has revealed promising advancement in the modelling and calibration of aftertreatment components, showing that GA can be used for complex problems, such as the calibration of a global kinetic scheme, with an acceptable computational effort

Application of Genetic Algorithm for the Calibration of the Kinetic Scheme of a Diesel Oxidation Catalyst Model / Millo, Federico; Rafigh, Mahsa; Sapio, Francesco; Barrientos, Eduardo J.; Ferreri, Paolo. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - ELETTRONICO. - (2018). [10.4271/2018-01-1762]

Application of Genetic Algorithm for the Calibration of the Kinetic Scheme of a Diesel Oxidation Catalyst Model

Federico Millo;Mahsa Rafigh;Francesco Sapio;
2018

Abstract

In this work, a methodology for building and calibrating the kinetic scheme for the 1D CFD model of a zone-coated automotive Diesel Oxidation Catalyst (DOC) by means of a Genetic Algorithm (GA) approach is presented. The methodology consists of a preliminary experimental activity followed by a modelling, optimization and validation process. The tested aftertreatment component presents zone coating, with the front brick side covered with Zeolites in order to ensure hydrocarbons trapping at low temperature, and Platinum Group Metal (PGM), while the rear brick side presents an alumina washcoat with a different PGM loading. Reactor scale samples representative of each coating zone were tested on a Synthetic Gas Bench (SGB), to fully characterize the component’s behavior in terms of Light-off and hydrocarbons (HC) storage for a wide range of inlet feed compositions and temperatures, representative of engine-out conditions. On the modeling side, a 1D-CFD model of the component was built in GT-SUITE environment and a global kinetic scheme was defined, based on the available literature, expressed in the Arrhenius form. A Genetic Algorithm optimization tool was then used to calibrate reaction rate parameters and active sites densities, by means of a sequential calibration strategy, categorizing the reaction model into several steps according to the experimental test protocol. In each step of the calibration, the number of independent variables was reduced as much as possible and the reactions could be isolated using primary single species tests, moving then to more complex gas mixtures to calibrate the mutual interaction of different species. The model was finally validated over experimental data, showing satisfactory predictive capabilities in terms of both light-off temperatures and oxidation rates, capturing the differences between different coating types as well. The presented methodology has revealed promising advancement in the modelling and calibration of aftertreatment components, showing that GA can be used for complex problems, such as the calibration of a global kinetic scheme, with an acceptable computational effort
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2713763
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