Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures

TOSUN E., AYDIN K., BİLGİLİ M.

ALEXANDRIA ENGINEERING JOURNAL, cilt.55, sa.4, ss.3081-3089, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55 Sayı: 4
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.aej.2016.08.011
  • Dergi Adı: ALEXANDRIA ENGINEERING JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3081-3089
  • Anahtar Kelimeler: Diesel engine, Biodiesel, Alcohol, Linear regression, Artificial neural network, PERFORMANCE, PREDICTION, EMISSIONS, BLENDS, OIL, COMBUSTION, PRESSURE, ANN
  • Çukurova Üniversitesi Adresli: Evet

Özet

This study deals with usage of linear regression (LR) and artificial neural network (ANN) modeling to predict engine performance; torque and exhaust emissions; and carbon monoxide, oxides of nitrogen (CO, NOx) of a naturally aspirated diesel engine fueled with standard diesel, peanut biodiesel (PME) and biodiesel-alcohol (EME, MME, PME) mixtures. Experimental work was conducted to obtain data to train and test the models. Backpropagation algorithm was used as a learning algorithm of ANN in the multilayered feedforward networks. Engine speed (rpm) and fuel properties, cetane number (CN), lower heating value (LHV) and density (q) were used as input parameters in order to predict performance and emission parameters. It was shown that while linear regression modeling approach was deficient to predict desired parameters, more accurate results were obtained with the usage of ANN. (C) 2016 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.

This study deals with usage of linear regression (LR) and artificial neural network (ANN) modeling to predict engine performance; torque and exhaust emissions; and carbon monoxide, oxides of nitrogen (CO, NOx) of a naturally aspirated diesel engine fueled with standard diesel, peanut biodiesel (PME) and biodiesel-alcohol (EME, MME, PME) mixtures. Experimental work was conducted to obtain data to train and test the models. Backpropagation algorithm was used as a learning algorithm of ANN in the multilayered feedforward networks. Engine speed (rpm) and fuel properties, cetane number (CN), lower heating value (LHV) and density (ρ) were used as input parameters in order to predict performance and emission parameters. It was shown that while linear regression modeling approach was deficient to predict desired parameters, more accurate results were obtained with the usage of ANN.