Artificial neural network based modeling of heated catalytic converter performance

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. 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 statistical coefficient of multiple determinations for the investigated cases is about 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.