Abstract
This research shows the development of a mathematical model applied to the estimation of pollutant indices in two different phases of operation of the internal combustion engine (ICM) by means of artificial neural networks (ANN). For data collection road and idle tests are performed which are recorded by the freematics one+ programmable device and the gas analyzer itself which are used to train the neural networks and generate a database with which it is possible to determine in which operating condition M1 category vehicles produce the highest pollution rates. The ANN results show a high similarity index in relation to the behavior of vehicles, obtaining an effective model that fits the characteristics of the city’s vehicle fleet. In addition, it is determined that during the first twelve minutes the levels of polluting gases reach extremely high values.
| Translated title of the contribution | Mathematical model for the estimation of pollutants from category m1 vehicles in cold and hot operation stages in the city of cuenca |
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| Original language | Spanish |
| Pages (from-to) | 366-375 |
| Number of pages | 10 |
| Journal | RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao |
| Volume | 2020 |
| Issue number | E30 |
| State | Published - Jun 2020 |
| Externally published | Yes |