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Applied Thermal Engineering xxx (2005) xxx–xxx
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Artificial neural network based modeling of heated catalytic converter performance
M. Ali Akcayol
a b

a,*

, Can Cinar

b

Department of Computer Engineering, Faculty of Engineering & Architecture, Gazi University, Maltepe, 06570 Ankara, Turkey Department of Mechanical Education,Faculty of Technical Education, Gazi University, Besevler, 06500 Ankara, Turkey Received 23 August 2004; accepted 17 December 2004

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 effectivelyremove 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. In this study, to overcome these difficulties, an artificial neural network (ANN) is used for prediction of catalyst temperature, HC emissions and CO emissions.The training data for ANN is obtained from experimental measurements. In comparison of performance analysis of ANN, the deviation coefficients of standard and heated catalyst temperature, standard and heated catalyst HC emissions, and standard and heated catalyst CO emissions for the test conditions are less than 4.925%, 1.602%, 4.798%, 4.926%, 4.82% and 4.938%, respectively. The statisticalcoefficient of multiple determinations for the investigated cases is about

*

Corresponding author. Tel.: +90 312 231 74 00/2123; fax: +90 312 230 84 34. E-mail address: akcayol@gazi.edu.tr (M. Ali Akcayol). URL: http://w3.gazi.edu.tr/~akcayol (M. Ali Akcayol).

1359-4311/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.applthermaleng.2004.12.014

ARTICLE IN PRESS

2M. Ali Akcayol, C. Cinar / Applied Thermal Engineering xxx (2005) xxx–xxx

0.9984–0.9997. The degree of accuracy is acceptable in predicting the parameters of the system. So, it can be concluded that ANN provides a feasible method in predicting the system parameters. Ó 2005 Elsevier Ltd. All rights reserved.
Keywords: Artificial neural network; Catalytic converter; Cold start

1.Introduction Because of the growing number of vehicles running all over the world, the problem of urban air pollution has been gained so much importance [1]. The spark ignition engine exhaust gases contain nitrogen oxides (NOx), CO and organic compounds, which are unburned or partially burned HCs. CO and HC occur because the combustion efficiency is lower than 100% due to incomplete mixing of the gases andthe wall quenching effects of the colder cylinder walls. The NOx is formed during the very high temperatures of the combustion process [2,3]. Improvements in engine design, microprocessor controlled fuel injection and ignition systems have been substantially reducing the pollutant emissions for two decades in spark ignition engines. However, further reductions in exhaust emissions can be obtained byremoving pollutants in the exhaust system. TWCÕs that controls the pollutant emissions of HC, CO and NOx are an effective way to reduce exhaust emissions [3,4]. But the requirements of future emission standards cannot be met by conventional TWC, as they cannot efficiently remove HC and CO from the outlet of internal combustion engines in the cold-start phase [5,6]. The efficiency of a catalytic...