Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

Year 2017, , 15 - 23, 20.03.2017

Erdi Tosun , Tayfun Ozgur , Ceyla Ozgur Mustafa Ozcanli , Hasan Serin , Kadir Aydin

https://doi.org/10.26701/ems.320490

Cited By: 24

Abstract

In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticle additives  was  modeled  by  regression  analysis,  artificial  neural  network  (ANN)  and  adaptive neuro  fuzzy  inference  system  (ANFIS)  methods.  Cetane  number  (CN)  and  engine  speed (rpm) were selected as input parameters for estimation of carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions. The results of estimation techniques were compared with each other and they showed that regression analysis was not accurate enough for prediction. On the other hand, ANN and ANFIS modelling techniques gave more accurate results with respect to regression analysis; linear and non-linear. Especially ANFIS models can be suggested as estimation method with minimum error compared to experimental results. 

Keywords

Adaptive neuro fuzzy inference system, Artificial neural network, Diesel engine, Regression analysis

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