Fuel and Combustion

Fuel and Combustion

Modeling the effect of adding oxygen functionalized multi-walled carbon nanotube to diesel, biodiesel, and bioethanol fuel blends on performance and emission of a diesel engine using artificial neural network

Document Type : Original Article

Authors
leila shakeri 1
ali asghari 2
Ahmad Taghizadeh-Alisaraei 3
1 Graduated student, Biosystems Engineering Dept., Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2 Assistant Professor, Biosystems Engineering Dept., Gorgan University of Agricultural Sciences and Natural Resources,
3 هیات علمی
Abstract
Blended biofuels such as biodiesel and bioethanol besides adding carbon nanotubes as catalysts to diesel fuel significantly improve engine performance and reduce emissions. In this study, biodiesel (5%) was initially added to the diesel fuel to evaluate engine performance and its emissions. The studied fuels were prepared as MWENT-COOH nanoparticles (30, 60 & 90 ppm) were added to the fuels of B5 (5% biodiesel and 95% diesel) and E6 (6% bioethanol and 94% diesel) and E3 (3% bioethanol and 97% diesel). Experiments were performed in triplicates and a multilayer feed-forward back-propagation (FFBP) artificial neural network (ANN) was used for modeling the obtained results. Fuel type, engine speed, density, viscosity, the thermal value of the fuel, inlet manifold pressure, fuel consumption, exhaust gas temperature, oil temperature, oxygen in the exhaust gases, relative humidity and pressure of inlet air were considered as independent or inlet layer parameters. Output layer parameters included engine performance and emission. The results represented the decrease in emissions of CO and unburned hydrocarbons and specific fuel consumption as well as an increase in nitrogen oxides emissions. Considering the R^2 and MSE, the ANN model based on the sigmoid learning function was introduced as the optimal one in comparison to the linear and hyperbolic tangent networks. The values of the regression coefficient (R^2) were also obtained for training, evaluation, and testing of the optimal network model. In conclusion, it can be mentioned that ANN was the most effective model in simulating the obtained data and investigating the sensitivity coefficient.
Keywords
Engine performance
Engine emissions
carbon nanotubes
Artificial neural network modeling
Subjects
  1. A. Taghizadeh-Alisaraei, H. A. Assar, B. Ghobadian and A. Motevali, “Potential of biofuel production from pistachio waste in Iran,” Renewable and Sustainable Energy Reviews, Vol. 72, 2017, pp. 510-522.
  2. A. K. Agarwal, “Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines,” Progress in Energy and Combustion Science, Vol. 33, 2007, pp. 233–71.
  3. J. Chen, R. Bond Randall and R. Bond, “Improved automated diagnosis of misfire in internal combustion engines based on simulation models,” Mechanical Systems and Signal Processing, Vol. 64-65, 2015, pp. 58–83.
  4. Z. H. U. Jian-yuan and J. Y. Zhu, “Detection of cylinder pressure in diesel engines using cylinder head vibration and time series methods,” Journal of Marine Science and Application, Vol. 6, 2007, pp. 8-12.
  5. A. Kirillov, O. Kirillova and S. Kirillov, “Algorithmic method of analysis of time series data for definition of prognostic parameters of engine fault,” 3rd International Conference on Advanced Computer Control (ICACC), Harbin, 2011, pp. 138-142.
  6. M. K. D. Kiani et al., Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends,” Energy, Vol. 35, 2010, pp. 65-69.
  7. B. Ghobadian, H. Rahimi, A. M. Nikbakht, G. Najafi and T. F. Yusaf, “Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network,” Renewable energy,Vol. 34, No. 4, 2009, pp. 976-982.
  8. R. Beale and T. Jackson, Neural Computing: An Introduction, Taylor and france, CRC Press, 1998.
  9. M. Menhaj, Foundation of Artifitioal Neural Networks, Master Section, Computer departmaent, Amir Kabir univercity, 2000.
  10. M. Fathi, M. Mohebbi and S. M. A. Razavi. “Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit,” Food and Bioprocess Technology, Vol. 4, 2011, pp. 1357–66.
  11. L. Pan, Q. Zhang, W. Zhang, Y. Sun, P. Hu and K. Tu. “Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network,” Food Chemistry, Vol. 192, 2016, pp. 134–41.
  12. M. Canakci, A. Erdil and E. Arcaklioğlu, “Performance and exhaust emissions of a biodiesel engine,” Applied Energy, Vol. 83, 2006, pp. 594-605.
  13. M. Dehghani , B. Ghobadian, Gh. Najafi, M. R. Sabzimaleki and F. Jaliliantabar, “Performance and Exhaust Emissions of a SI Two-stroke Engine with Biolubricants using Artificial Neural Network,” Energy Procedia, Vol. 75, 2015, pp. 3-9.
  14. S. H. Hoseini, A. Taghizadeh, B. Ghobadian and A. Abaszadeh, “Modeling the effects of Carbon nanotubes added to diesel-biodiesel fuel blends on performance and emissions of a diesel engine using artificial neural network,” Fuel and combisution Journal, Vol. 10, 2017, pp. 1-16.
  15. S. Das, A. Routray and A. K. Deb, “Hyperspectral Unmixing by Nuclear Norm Difference Maximization based Dictionary Pruning,” 14th IEEE India Council International Conference, Roorkee, India, (INDICON),2017, pp. 1-5.
  16. E. Johnston BAS, Design, Optimization and Testing of a Combined Tri-Axial Polarized Energy Dispersive X-Ray Fluorescence and Energy Dispersive X-Ray Diffraction System for Biological Sample Classification, Doctor of Philosophy section, McMaster University.
  17. H. Lu, H. Zheng, H. Lou, L. Jiang,Y. Chen and S. Fang. “Using neural networks to estimate the losses of ascorbic acid, total phenols, flavonoid, and antioxidant activity in asparagus during thermal treatments,” Journal of Agricultural and Food Chemistry, Vol. 58, 2010, pp. 2995–3001.
  18. A. Hosu, C. M. Cristea, C. Cimpoiu, “Analysis of total phenolic, flavonoids, anthocyanins and tannin content in Romanian red wines: Prediction of antioxidant activities and classification of wines using artificial neural networks,” Food Chemistry, Vol. 150, 2014, pp. 113–8.
  19. W. A. Balogun, M. E. Salami, Aibinu, Y. M. Mustafah and S. SIB, “Mini Review: Artificial Neural Network Application on Fruit and Vegetables Quality Assessment,” International Journal of Scientific & Engineering Research, Vol. 5, 2014, pp. 702–8.
  20. B. Soleimanzadeh, H. Hemati, M. Yolmeh and F. Salehi, “GA-ANN and ANFIS models and salmonella enteritidis inactivation by ultrasound,” Journal of Food Safety, Vol. 35, 2015, pp. 220–6.
  21. F. Salehi, A. Gohari Ardabili, A. Nemati, “Latifi Darab R. Modeling of strawberry drying process using infrared dryer by genetic algorithm–artificial neural network method,” Journal Food Science and Technology, Vol. 14, 2017, pp. 105–14.
  22. M. Azadbakht, M. Vahedi Torshizi and A. Ziaratban, “Application of Artificial Neural Network (ANN ) in predicting mechanical properties of canola stem under shear loading,” Agricultural Engineering International: CIGR Journal, Vol. 18, 2016, pp. 413–24.
  23. F. Salehi and S. M. A. Razavi. “Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks,” Desalination and Water Treatment, Vol. 41, 2012, pp. 95–104.
  24. B. Khoshnevisan, Sh. Rafiee and M. Omid. “Prediction of environmental indices of Iran wheat production using artificial neural networks,” International Journal of Energy and Environment, Vol. 4, 2013, pp. 339–48.
  25. M. Azadbakht, H. Aghili, A. Ziaratban and M. Vehedi Torshizi. “Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes,” Energy, Vol. 120, 2017, pp. 947–58.