Araştırma Makalesi
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Yıl 2024, Cilt: 7 Sayı: 1, 103 - 111, 30.04.2024

Beytullah Eren , İdris Cesur

https://doi.org/10.35377/saucis...1444155

Öz

Kaynakça

  • [1] H. Aydin and C. İlkiliç, 'Air pollution, pollutant emissions and harmfull 'effects', J. Eng. Technol., vol. 1, no. 1, Art. no. 1, Dec. 2017.
  • [2] F. Kelen, ‘Motorlu Taşıt Emisyonlarının İnsan Sağlığı ve Çevre Üzerine Etkileri’, Üzüncü Il Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 19, no. 1–2, Art. no. 1–2, Nov. 2014.
  • [3] P. Gireesh Kumar, P. Lekhana, M. Tejaswi, and S. Chandrakala, 'Effects of vehicular emissions on the urban environment- a state of the 'art', Mater. Today Proc., vol. 45, pp. 6314–6320, Jan. 2021, doi: 10.1016/j.matpr.2020.10.739.
  • [4] E. Ogur and S. Kariuki, 'Effect of Car Emissions on Human Health and the 'Environment', Int. J. Appl. Eng. Res., vol. 9, pp. 11121–11128, Jan. 2014.
  • [5] K. A. Bello, O. Awogbemi, and M. G. Kanakana-Katumba, 'Assessment of Alternative Fuels for Sustainable Road 'Transportation', presented at the 2023 IEEE 11th International Conference on Smart Energy Grid Engineering, SEGE 2023, 2023, pp. 7–15. doi: 10.1109/SEGE59172.2023.10274583.
  • [6] A. S. Chadha, Y. Shinde, N. Sharma, and P. K. De, 'Predicting CO2 Emissions by Vehicles Using Machine 'Learning', in Data Management, Analytics and Innovation, S. Goswami, I. S. Barara, A. Goje, C. Mohan, and A. M. Bruckstein, Eds., in Lecture Notes on Data Engineering and Communications Technologies. Singapore: Springer Nature, 2023, pp. 197–207. doi: 10.1007/978-981-19-2600-6_14.
  • [7] Z. Xu, Y. Kang, and W. Lv, 'Analysis and prediction of vehicle exhaust emission using 'ANN', Jul. 2017, pp. 4029–4033. doi: 10.23919/ChiCC.2017.8027988.
  • [8] O. S. Azeez, B. Pradhan, and H. Z. M. Shafri, 'Vehicular CO Emission Prediction Using Support Vector Regression Model and 'GIS', Sustainability, vol. 10, no. 10, Art. no. 10, Oct. 2018, doi: 10.3390/su10103434.
  • [9] M. Singh and R. K. Dubey, 'Deep Learning Model Based CO2 Emissions Prediction Using Vehicle Telematics Sensors 'Data', IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 768–777, Jan. 2023, doi: 10.1109/TIV.2021.3102400.
  • [10] F. J. J. Shobana Bai, 'A Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel 'Engine', Fuel, vol. 341, p. 127578, Jun. 2023, doi: 10.1016/j.fuel.2023.127578.
  • [11] A. L. Hananto et al., 'Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable 'biofuel', Results Eng., vol. 19, p. 101334, Sep. 2023, doi: 10.1016/j.rineng.2023.101334.
  • [12] K. Ramalingam et al., 'Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous 'biofuels', Environ. Sci. Pollut. Res., vol. 27, no. 20, pp. 24702–24722, Jul. 2020, doi: 10.1007/s11356-019-06222-7.
  • [13] S. Ding, H. Li, C. Su, J. Yu, and F. Jin, 'Evolutionary artificial neural networks: a 'review', Artif. Intell. Rev., vol. 39, no. 3, pp. 251–260, Mar. 2013, doi: 10.1007/s10462-011-9270-6.
  • [14] V. Eyupoglu, B. Eren, and E. Dogan, 'Prediction of Ionic Cr (VI) Extraction Efficiency in Flat Sheet Supported Liquid Membrane Using Artificial Neural Networks (ANNs)', Int. J. Environ. Res., vol. 4, no. 3, pp. 463–470, SUM 2010.
  • [15] Y. Wu and J. Feng, 'Development and Application of Artificial Neural 'Network', Wirel. Pers. Commun., vol. 102, no. 2, pp. 1645–1656, Sep. 2018, doi: 10.1007/s11277-017-5224-x.
  • [16] J. Zou, Y. Han, and S.-S. So, 'Overview of Artificial Neural 'Networks', in Artificial Neural Networks: Methods and Applications, D. J. Livingstone, Ed., in Methods in Molecular BiologyTM. , Totowa, NJ: Humana Press, 2009, pp. 14–22. doi: 10.1007/978-1-60327-101-1_2.
  • [17] S. Vieira, W. H. L. Pinaya, and A. Mechelli, 'Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and 'applications', Neurosci. Biobehav. Rev., vol. 74, no. Pt A, pp. 58–75, Mar. 2017, doi: 10.1016/j.neubiorev.2017.01.002.
  • [18] D. J. Livingstone, Ed., Artificial Neural Networks, vol. 458. in Methods in Molecular BiologyTM, vol. 458. Totowa, NJ: Humana Press, 2009. doi: 10.1007/978-1-60327-101-1.
  • [19] M. Zakaria, M. AL-Shebany, and S. Sarhan, 'Artificial Neural Network : A Brief 'Overview', vol. 4, no. 2, 2014.
  • [20] E. Dogan, A. Ates, E. C. Yilmaz, and B. Eren, 'Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen 'Demand', Environ. Prog., vol. 27, no. 4, pp. 439–446, Dec. 2008, doi: 10.1002/ep.10295.
  • [21] B. Eren, İ. Aksangür, and C. Erden, 'Predicting next hour fine particulate matter (PM2.5) in the Istanbul Metropolitan City using deep learning algorithms with time windowing 'strategy', Urban Clim., vol. 48, p. 101418, Mar. 2023, doi: 10.1016/j.uclim.2023.101418.

Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation

Yıl 2024, Cilt: 7 Sayı: 1, 103 - 111, 30.04.2024

Beytullah Eren , İdris Cesur

https://doi.org/10.35377/saucis...1444155

Öz

In recent years, increasing concerns about vehicle emissions' environmental and public health impacts have led to the desire to use eco-friendly fuels as alternatives to traditional fossil fuels. Biofuels, hydrogen, and electric power offer lower greenhouse gas emissions and improved air quality, resulting in their development and adoption globally. Predicting vehicle emissions using these fuels is crucial for assessing their environmental benefits. This study proposes using artificial neural networks (ANN), a machine learning technique, to accurately predict vehicle emissions associated with eco-friendly fuels across different compositions and engine speeds. The ANN model has a strong correlation between predicted and observed emissions values, indicating the effectiveness of its model. The research underscores the importance of adopting innovative approaches to address environmental challenges and promote sustainable transportation solutions. This study contributes to reducing the adverse effects of vehicle emissions on air quality and public health by assisting policymakers, car manufacturers, and city planners in making effective decisions. It promotes environmental sustainability by providing valuable insights into vehicle emissions prediction and guiding the development of eco-friendly fuels for a more efficient transportation system.

Anahtar Kelimeler

Vehicle Emissions Prediction Eco-Friendly Fuels Artificial Neural Network Environmental Sustainability Sustainable Transportation

Kaynakça

  • [1] H. Aydin and C. İlkiliç, 'Air pollution, pollutant emissions and harmfull 'effects', J. Eng. Technol., vol. 1, no. 1, Art. no. 1, Dec. 2017.
  • [2] F. Kelen, ‘Motorlu Taşıt Emisyonlarının İnsan Sağlığı ve Çevre Üzerine Etkileri’, Üzüncü Il Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 19, no. 1–2, Art. no. 1–2, Nov. 2014.
  • [3] P. Gireesh Kumar, P. Lekhana, M. Tejaswi, and S. Chandrakala, 'Effects of vehicular emissions on the urban environment- a state of the 'art', Mater. Today Proc., vol. 45, pp. 6314–6320, Jan. 2021, doi: 10.1016/j.matpr.2020.10.739.
  • [4] E. Ogur and S. Kariuki, 'Effect of Car Emissions on Human Health and the 'Environment', Int. J. Appl. Eng. Res., vol. 9, pp. 11121–11128, Jan. 2014.
  • [5] K. A. Bello, O. Awogbemi, and M. G. Kanakana-Katumba, 'Assessment of Alternative Fuels for Sustainable Road 'Transportation', presented at the 2023 IEEE 11th International Conference on Smart Energy Grid Engineering, SEGE 2023, 2023, pp. 7–15. doi: 10.1109/SEGE59172.2023.10274583.
  • [6] A. S. Chadha, Y. Shinde, N. Sharma, and P. K. De, 'Predicting CO2 Emissions by Vehicles Using Machine 'Learning', in Data Management, Analytics and Innovation, S. Goswami, I. S. Barara, A. Goje, C. Mohan, and A. M. Bruckstein, Eds., in Lecture Notes on Data Engineering and Communications Technologies. Singapore: Springer Nature, 2023, pp. 197–207. doi: 10.1007/978-981-19-2600-6_14.
  • [7] Z. Xu, Y. Kang, and W. Lv, 'Analysis and prediction of vehicle exhaust emission using 'ANN', Jul. 2017, pp. 4029–4033. doi: 10.23919/ChiCC.2017.8027988.
  • [8] O. S. Azeez, B. Pradhan, and H. Z. M. Shafri, 'Vehicular CO Emission Prediction Using Support Vector Regression Model and 'GIS', Sustainability, vol. 10, no. 10, Art. no. 10, Oct. 2018, doi: 10.3390/su10103434.
  • [9] M. Singh and R. K. Dubey, 'Deep Learning Model Based CO2 Emissions Prediction Using Vehicle Telematics Sensors 'Data', IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 768–777, Jan. 2023, doi: 10.1109/TIV.2021.3102400.
  • [10] F. J. J. Shobana Bai, 'A Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel 'Engine', Fuel, vol. 341, p. 127578, Jun. 2023, doi: 10.1016/j.fuel.2023.127578.
  • [11] A. L. Hananto et al., 'Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable 'biofuel', Results Eng., vol. 19, p. 101334, Sep. 2023, doi: 10.1016/j.rineng.2023.101334.
  • [12] K. Ramalingam et al., 'Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous 'biofuels', Environ. Sci. Pollut. Res., vol. 27, no. 20, pp. 24702–24722, Jul. 2020, doi: 10.1007/s11356-019-06222-7.
  • [13] S. Ding, H. Li, C. Su, J. Yu, and F. Jin, 'Evolutionary artificial neural networks: a 'review', Artif. Intell. Rev., vol. 39, no. 3, pp. 251–260, Mar. 2013, doi: 10.1007/s10462-011-9270-6.
  • [14] V. Eyupoglu, B. Eren, and E. Dogan, 'Prediction of Ionic Cr (VI) Extraction Efficiency in Flat Sheet Supported Liquid Membrane Using Artificial Neural Networks (ANNs)', Int. J. Environ. Res., vol. 4, no. 3, pp. 463–470, SUM 2010.
  • [15] Y. Wu and J. Feng, 'Development and Application of Artificial Neural 'Network', Wirel. Pers. Commun., vol. 102, no. 2, pp. 1645–1656, Sep. 2018, doi: 10.1007/s11277-017-5224-x.
  • [16] J. Zou, Y. Han, and S.-S. So, 'Overview of Artificial Neural 'Networks', in Artificial Neural Networks: Methods and Applications, D. J. Livingstone, Ed., in Methods in Molecular BiologyTM. , Totowa, NJ: Humana Press, 2009, pp. 14–22. doi: 10.1007/978-1-60327-101-1_2.
  • [17] S. Vieira, W. H. L. Pinaya, and A. Mechelli, 'Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and 'applications', Neurosci. Biobehav. Rev., vol. 74, no. Pt A, pp. 58–75, Mar. 2017, doi: 10.1016/j.neubiorev.2017.01.002.
  • [18] D. J. Livingstone, Ed., Artificial Neural Networks, vol. 458. in Methods in Molecular BiologyTM, vol. 458. Totowa, NJ: Humana Press, 2009. doi: 10.1007/978-1-60327-101-1.
  • [19] M. Zakaria, M. AL-Shebany, and S. Sarhan, 'Artificial Neural Network : A Brief 'Overview', vol. 4, no. 2, 2014.
  • [20] E. Dogan, A. Ates, E. C. Yilmaz, and B. Eren, 'Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen 'Demand', Environ. Prog., vol. 27, no. 4, pp. 439–446, Dec. 2008, doi: 10.1002/ep.10295.
  • [21] B. Eren, İ. Aksangür, and C. Erden, 'Predicting next hour fine particulate matter (PM2.5) in the Istanbul Metropolitan City using deep learning algorithms with time windowing 'strategy', Urban Clim., vol. 48, p. 101418, Mar. 2023, doi: 10.1016/j.uclim.2023.101418.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çevresel Olarak Sürdürülebilir Mühendislik
Bölüm Makaleler
Yazarlar

Beytullah Eren 0000-0001-6747-7004

İdris Cesur 0000-0001-7487-5676

Erken Görünüm Tarihi 27 Nisan 2024
Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 28 Şubat 2024
Kabul Tarihi 3 Nisan 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 1

Kaynak Göster

IEEE B. Eren ve İ. Cesur, “Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation”, SAUCIS, c. 7, sy. 1, ss. 103–111, 2024, doi: 10.35377/saucis...1444155.
 Kapak Resmi İndir

29070  The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License