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Year 2024, , 103 - 111, 30.04.2024

Beytullah Eren , İdris Cesur

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

Abstract

References

  • [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.
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  • [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

Year 2024, , 103 - 111, 30.04.2024

Beytullah Eren , İdris Cesur

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

Abstract

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.

Keywords

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

References

  • [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.
There are 21 citations in total.

Details

Primary Language English
Subjects Environmentally Sustainable Engineering
Journal Section Articles
Authors

Beytullah Eren 0000-0001-6747-7004

İdris Cesur 0000-0001-7487-5676

Early Pub Date April 27, 2024
Publication Date April 30, 2024
Submission Date February 28, 2024
Acceptance Date April 3, 2024
Published in Issue Year 2024

Cite

IEEE B. Eren and İ. Cesur, “Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation”, SAUCIS, vol. 7, no. 1, pp. 103–111, 2024, doi: 10.35377/saucis...1444155.

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