dc.creator | Pontikakis, G. N. | en |
dc.creator | Stamatelos, A. M. | en |
dc.date.accessioned | 2015-11-23T10:45:56Z | |
dc.date.available | 2015-11-23T10:45:56Z | |
dc.date.issued | 2004 | |
dc.identifier | 10.1243/0954407042707696 | |
dc.identifier.issn | 0954-4070 | |
dc.identifier.uri | http://hdl.handle.net/11615/32409 | |
dc.description.abstract | The need to deliver fast-in-market and right-first-time design for ultra-low-emission vehicles at a reasonable cost is driving the automotive industries to invest significant manpower in computer-aided design and optimization of exhaust after-treatment systems. To serve the above goals, an already developed engineering model for the three-way catalytic converter kinetic behaviour is linked with a genetic algorithm optimization procedure, for fast and accurate estimation of the set of tuneable kinetic parameters that describe the chemical behaviour of each specific washcoat formulation. The genetic-algorithm-based optimization procedure utilizes a purpose-designed performance measure that allows an objective assessment of model prediction accuracy against a set of experimental data that represent the behaviour of the specific washcoat formulation over a typical legislated test procedure. The identification methodology is tested on a demanding case Study, and the best-fit parameters demonstrate high accuracy in matching the measured test data. The results are definitely more accurate than those Usually obtained by manual or gradient-based tuning of the parameters. Moreover, the set of parameters identified by the genetic algorithm methodology is proven to describe in a valid way the chemical kinetic behaviour of the specific catalyst, and this is tested by the successful prediction of the performance of a smaller-size converter. The parameter estimation methodology developed fits in an integrated computer-aided engineering methodology assisting the design optimization of catalytic exhaust systems that extends all the way through from model development to parameter estimation, and quality assurance of test data. | en |
dc.source.uri | <Go to ISI>://WOS:000228672000008 | |
dc.subject | catalytic converter kinetic model | en |
dc.subject | genetic algorithm | en |
dc.subject | catalytic exhaust | en |
dc.subject | systems | en |
dc.subject | MATHEMATICAL-MODELS | en |
dc.subject | TEMPERATURE | en |
dc.subject | EMISSIONS | en |
dc.subject | OXIDATION | en |
dc.subject | Engineering, Mechanical | en |
dc.subject | Transportation Science & Technology | en |
dc.title | Identification of catalytic converter kinetic model using a genetic algorithm approach | en |
dc.type | journalArticle | en |