Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloReal Time Prediction of Particle Sizing at the Exhaust of a Diesel Engine by Using a Neural Network Model
Anno di pubblicazione2017
Formato
  • Elettronico
  • Cartaceo
Autore/iTaglialatela F.; Lavorgna M.; Di Iorio S.; Mancaruso E.; Vaglieco B.M.
Affiliazioni autoriTaglialatela F., Lavornia M.: STMicroelectronics, , Italy
Autori CNR e affiliazioni
  • EZIO MANCARUSO
  • SILVANA DI IORIO
  • BIANCA MARIA VAGLIECO
Lingua/e
  • inglese
AbstractIn order to meet the increasingly strict emission regulations, several solutions for NOx and PM emissions reduction have been studied. Exhaust gas recirculation (EGR) technology has become one of the more used methods to accomplish the NOx emissions reduction. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle size and density. These latter have a great importance both for the optimal functioning of after-treatment systems, but also for the adverse effects that small particles have on human health. Epidemiological studies, in fact, highlighted that the toxicity of particulate particles increases as the particle size decreases. The aim of this paper is to present a Neural Network model able to provide real time information about the characteristics of exhaust particles emitted by a Diesel engine. In particular, the model acts as a virtual sensor able to estimate the concentration of particles with a specific aerodynamic diameter on the basis of some engine parameters such as engine speed, engine load and EGR ratio.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali7
RivistaSAE International journal of engines (Print)
Attiva dal 2009
Editore: SAE International - Warrendale, PA
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1946-3936
Titolo chiave: SAE International journal of engines (Print)
Titolo proprio: SAE International journal of engines. (Print)
Titolo alternativo: Journal of engines (Print)
Numero volume della rivista10
Fascicolo della rivista4
DOI10.4271/2017-24-0051
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85028304432)
Parole chiaveNEURAL NETWORK MODEL, PARTICLE EMISSIONS PREDICTION, DIESEL ENGINE.
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-85028304432&partnerID=q2rCbXpz
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni13th International Conference on Engines & Vehicles - Capri (italy) 10-14 settembre 2017 SAE PAPER 2017-24-0051
Strutture CNR
  • IM — Istituto motori
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
2017P2944 (documento privato )
Tipo documento: application/pdf