期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2015
卷号：4
期号：2
页码：604
DOI：10.15680/IJIRSET.2015.0402080
出版社：S&S Publications
摘要：Catalytic converters are the most effective means of reducing pollutant emissions from internalcombustion engines under normal operating conditions. But the future emission requirements cannot be met by threeway catalysts (TWC) as they cannot effectively remove hydrocarbon (HC) and carbon monoxide (CO) emissions fromthe outlet of internal combustion engines in the cold-start phase. Therefore, significant efforts have been put inimproving the cold-start behavior of catalytic converters. In the experimental study, to improve cold-start performanceof catalytic converter for HC and CO, a burner heated catalyst (BHC) has been tested in a four stroke, spark ignitionengine. The modeling of catalytic converter performance of the engine during cold start is a difficult task. It involvescomplicated heat transfer and processes and chemical reactions at both the catalytic converter and exhaust pipe. In thisstudy, to overcome these difficulties, multi-output adaptive neuro-fuzzy inference system (M-ANFIS) is used forprediction of catalyst temperature, HC emissions and CO emissions. The training data for M-ANFIS is obtained fromexperimental measurements. In comparison of performance analysis of M-ANFIS the deviation coefficients of standardand heated catalyst temperature, standard and heated catalyst HC emissions, and standard and heated catalyst COemissions for the test conditions are less than 4.825%, 1.502%, 4.801%, 4.725%, 4.79% and 4.898%, respectively. Thestatistical coefficient of multiple determinations for the investigated cases is about 0.9981–0.9998. The degree ofaccuracy is acceptable in predicting the parameters of the system. So, it can be concluded that M-ANFIS provides afeasible method in predicting the system parameters.In this paper we propose a new type of multi output adaptive neuro-fuzzy inference system (M-ANFIS) with severaloutputs. To proves its performances, the proposed multi output ANFIS is used to make the approximation at the sametime of three different functions. Simulation results show that this neuro-fuzzy system can approximate, with thedesired precision, these three functions.
