Artificial neural network based modeling of heated catalytic converter performance


Akcayol M. A. , Cinar C.

APPLIED THERMAL ENGINEERING, vol.25, pp.2341-2350, 2005 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25
  • Publication Date: 2005
  • Doi Number: 10.1016/j.applthermaleng.2004.12.014
  • Journal Name: APPLIED THERMAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.2341-2350
  • Keywords: artificial neural network, catalytic converter, cold start, PERSPECTIVES, SYSTEMS

Abstract

Catalytic converters are the most effective means of reducing pollutant emissions from internal combustion engines under normal operating conditions. But the future emission requirements cannot be met by three way catalysts (TWC) as they cannot effectively remove hydrocarbon (HC) and carbon monoxide (CO) emissions from the outlet of internal combustion engines in the cold-start phase. Therefore, significant efforts have been put in improving the cold-start behavior of catalytic converters. In the experimental study, to improve cold-start performance of catalytic converter for HC and CO, a burner heated catalyst (BHC) has been tested in a four stroke, spark ignition engine. The modeling of catalytic converter performance of the engine during cold start is a difficult task. It involves complicated heat transfer and processes and chemical reactions at both the catalytic converter and exhaust pipe.