@article{article_320490, title={Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives}, journal={European Mechanical Science}, volume={1}, pages={15–23}, year={2017}, DOI={10.26701/ems.320490}, author={Tosun, Erdi and Ozgur, Tayfun and Ozgur, Ceyla and Ozcanli, Mustafa and Serin, Hasan and Aydin, Kadir}, keywords={Adaptive neuro fuzzy inference system,Artificial neural network,Diesel engine,Regression analysis}, abstract={<p> <span style="font-size:12px;">In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticle  </span> <span style="font-size:12px;">additives  was  modeled  by  regression  analysis,  artificial  neural  network  (ANN)  and  adaptive  </span> <span style="font-size:12px;">neuro  fuzzy  inference  system  (ANFIS)  methods.  Cetane  number  (CN)  and  engine  speed  </span> <span style="font-size:12px;">(rpm) were selected as input parameters for estimation of carbon monoxide (CO), oxides of  </span> <span style="font-size:12px;">nitrogen (NOx), and carbon dioxide (CO2) emissions. The results of estimation techniques were  </span> <span style="font-size:12px;">compared with each other and they showed that regression analysis was not accurate enough  </span> <span style="font-size:12px;">for prediction. On the other hand, ANN and ANFIS modelling techniques gave more accurate  </span> <span style="font-size:12px;">results with respect to regression analysis; linear and non-linear. Especially ANFIS models can  </span> <span style="font-size:12px;">be suggested as estimation method with minimum error compared to experimental results.  </span> </p>}, number={1}, publisher={Ahmet ÇALIK}