Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks

Cenk Kaya; Hüseyin Elçiçek

Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks

Öz

In this study, performance and emission predictions of four stroke, naturally aspirated, water cooled single cylinder diesel engine were carried out with created artificial neural networks. Partial load experiments of the engine have been conducted and power, specific fuel consumption values and CO2, CO, NOx emissions were recorded. Obtained values were used in modeling studies. In the study, 59 data were used, 80% of this data was used as training and 20% as test data. The data are modeled with multi layer, Back-Propagation (BP) and Radial Basis Function artificial neural network. According to the results obtained, the model predictions are consistent with the experimental results and it has been observed that emission, power and specific fuel consumption can be predicted with limited data of the engine such as speed and load. Also, best results for estimation of emissions as one of the most important problems of diesel engines, are obtained from BP compared to RBF.

Anahtar Kelimeler

Kaynakça

[1] A. Ekmekçioğlu, S. L. Kuzu, K. Ünlügençoğlu, and U. B. Çelebi, “Assessment of shipping emission factors through monitoring and modelling studies,” The Science of the total environment, vol. 743, 2020.
[2] M. T. Islam, F. Rashid, and A. Arefin, “Numerical analysis of the performance and NOx emission of a diesel engine fueled with algae biofuel-diesel blends,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 2021.
[3] G. Gonca, “Investigation of the influences of steam injection on the equilibrium combustion products and thermodynamic properties of bio fuels (biodiesels and alcohols),” Fuel, vol. 144, pp. 244–258, 2015.
[4] G. Gonca, “Effects of engine design and operating parameters on the performance of a spark ignition (SI) engine with steam injection method (SIM),” Applied Mathematical Modelling, vol. 44, pp. 655–675, Apr. 2017.
[5] M. K. Deh Kiani, B. Ghobadian, T. Tavakoli, A. M. Nikbakht, and G. Najafi, “Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends,” Energy, vol. 35, no. 1, pp. 65–69, 2010.
[6] B. Maaß, R. Stobart, and J. Deng, “Diesel engine emissions prediction using parallel neural networks,” Proceedings of the American Control Conference, pp. 1122–1127, 2009.
[7] H. Sevinc and H. Hazar, “Determining the effects of 2-ethylhexyl nitrate blend on isolated diesel engine attributes using the experimental and ANN approaches,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 2019.
[8] Q. Zhang and D. Tian, “Study of CWS/diesel dual fuel engine emissions by means of RBF neural network,” in Asia-Pacific Power and Energy Engineering Conference, APPEEC, 2010.

[9] P. Shanmugam, V. Sivakumar, A. Murugesan, and M. Ilangkumaran, “Performance and Exhaust Emissions of a Diesel Engine Using Hybrid Fuel with an Artificial Neural Network,” Energy Sources, Part A, vol. 33, pp. 1440–1450, 2011.
[10] G. Mao, C. Zhang, K. Shi, and P. Wang, “Prediction of the performance and exhaust emissions of ethanol-diesel engine using different neural network,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, vol. 0, no. 0, pp. 1–15, 2019.
[11] D. Babu, V. Thangarasu, and A. Ramanathan, “Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel,” Applied Energy, vol. 263, no. February, 2020.
[12] S. Raghuvaran, B. Ashok, B. Veluchamy, and N. Ganesh, “Evaluation of performance and exhaust emission of C.I diesel engine fuel with palm oil biodiesel using an artificial neural network,” Materials Today: Proceedings, no. xxxx, 2020.
[13] R. Kenanoğlu, M. K. Baltacıoğlu, M. H. Demir, and M. Erkınay Özdemir, “Performance &emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches,” International Journal of Hydrogen Energy, vol. 45, no. 49, pp. 26357– 26369, 2020.
[14] M. İ. ÖZMEN, Ö. CİHAN, A. KUTLAR, O. A. ÖZSOYSAL, and C. BAYKARA, “Modelling A Single-Rotor Wankel Engine Performance With Artificial Neural Network At Middle Speed Range,” International Journal of Automotive Science And Technology, vol. 4, no. 3, pp. 155– 163, 2020.
[15] M. Aydın, S. Uslu, and M. Bahattin Çelik, “Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimization,” Fuel, vol. 269, no. February, 2020.
[16] K. Ramalingam et al., “Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous biofuels,” Environmental Science and Pollution Research, vol. 27, no. 20, pp. 24702–24722, 2020.
[17] J. Castresana, G. Gabiña, L. Martin, and Z. Uriondo, “Comparative performance and emissions assessments of a single-cylinder diesel engine using artificial neural network and thermodynamic simulation,” Applied Thermal Engineering, no. October, p. 116343, 2020.
[18] C. S. Lage, S. de Morais Hanriot, and L. E. Zárate, “Using artificial neural networks to represent a diesel–biodiesel engine,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 42, no. 11, pp. 1–12, 2020.
[19] P. Madane and R. Panua, “Investigation of Performance of jatropha oil on diesel engine using Artificial Neural Network Model .,” pp. 522–528, 2018.
[20] K. s. v and S. K. Masimalai, “Predicting the performance and emission characteristics of a Mahua oil-hydrogen dual fuel engine using artificial neural networks,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, vol. 42, no. 23, pp. 2891–2910, 2020.
[21] M. KARAGÖZ, “ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends,” International Journal of Automotive Science And Technology, vol. 4, no. x, pp. 180–184, 2020.
[22] C. Kaya, Z. Aydin, G. Kökkülünk, and A. Safa, “Exergetic and exergoeconomic analyzes of compressed natural gas as an alternative fuel for a diesel engine,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 2020.

Ayrıntılar

Birincil Dil

Türkçe

Konular

Deniz Mühendisliği

Bölüm

Araştırma Makalesi

Yazarlar

Cenk Kaya Bu kişi benim
Palestine

Hüseyin Elçiçek
Türkiye

Yayımlanma Tarihi

30 Haziran 2021

Gönderilme Tarihi

6 Haziran 2021

Kabul Tarihi

-

Yayımlandığı Sayı

Yıl 2021 Cilt: 1 Sayı: 1

IZ

https://izlik.org/JA42MP78SL

Kaynak Göster

RIS / Bibtex

APA

Kaya, C., & Elçiçek, H. (2021). Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks. Journal of Marine and Engineering Technology, 1(1), 24-33. https://izlik.org/JA42MP78SL

AMA

1.Kaya C, Elçiçek H. Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks. JOINMET. 2021;1(1):24-33. https://izlik.org/JA42MP78SL

Chicago

Kaya, Cenk, ve Hüseyin Elçiçek. 2021. “Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks”. Journal of Marine and Engineering Technology 1 (1): 24-33. https://izlik.org/JA42MP78SL.

EndNote

Kaya C, Elçiçek H (01 Haziran 2021) Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks. Journal of Marine and Engineering Technology 1 1 24–33.

IEEE

[1]C. Kaya ve H. Elçiçek, “Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks”, JOINMET, c. 1, sy 1, ss. 24–33, Haz. 2021, [çevrimiçi]. Erişim adresi: https://izlik.org/JA42MP78SL

ISNAD

Kaya, Cenk - Elçiçek, Hüseyin. “Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks”. Journal of Marine and Engineering Technology 1/1 (01 Haziran 2021): 24-33. https://izlik.org/JA42MP78SL.

JAMA

1.Kaya C, Elçiçek H. Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks. JOINMET. 2021;1:24–33.

MLA

Kaya, Cenk, ve Hüseyin Elçiçek. “Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks”. Journal of Marine and Engineering Technology, c. 1, sy 1, Haziran 2021, ss. 24-33, https://izlik.org/JA42MP78SL.

Vancouver

1.Cenk Kaya, Hüseyin Elçiçek. Modeling of Diesel Engine Performance and Emission Using Artificial Neural Networks. JOINMET [Internet]. 01 Haziran 2021;1(1):24-33. Erişim adresi: https://izlik.org/JA42MP78SL