| Titre : | Real-time transient soot and NOx virtual sensors for diesel engine using neuro-fuzzy model tree and orthogonal least squares (2012) |
| Auteurs : | Rajit Johri, Auteur ; Ashwin Salvi, Auteur ; Zoran Filipi, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Transactions of the ASME . Journal of engineering for gas turbines and power (Vol. 134 N° 9, Septembre 2012) |
| Article en page(s) : | 09 p. |
| Note générale : | Génie mécanique |
| Langues : | Anglais |
| Index. décimale : | 620.1 (Essais des matériaux. Défauts des matériaux. Protection des matériaux) |
| Tags : | Diesel engine combustion Engine exhaust emission Neuro-fuzzy model tree NOx virtual sensors |
| Résumé : | Diesel engine combustion and emission formation is highly nonlinear and thus creates a challenge related to engine diagnostics and engine control with emission feedback. This paper presents a novel methodology to address the challenge and develop virtual sensing models for engine exhaust emission. These models are capable of predicting transient emissions accurately and are computationally efficient for control and optimization studies. The emission models developed in this paper belong to the family of hierarchical models, namely the “neuro-fuzzy model tree.” The approach is based on divide-and-conquer strategy, i.e., to divide a complex problem into multiple simpler subproblems, which can then be identified using a simpler class of models. Advanced experimental setup incorporating a medium duty diesel engine is used to generate training data. Fast emission analyzers for soot and NOx provide instantaneous engine-out emissions. Finally, the engine-in-the-loop is used to validate the models for predicting transient particulate mass and NOx. |
| DEWEY : | 620.1 |
| ISSN : | 0742-4795 |
| En ligne : | http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000009092806000001&idtype=cvips&gifs=Yes&ref=no |
