Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach
Abstract
Predicting engine efficiency for environmental sustainability is crucial in the automotive industry. Accurate estimation and optimization of engine efficiency aid in vehicle design decisions, fuel efficiency enhancement, and emission reduction. Traditional methods for predicting efficiency are challenging and time-consuming, leading to the adoption of artificial intelligence techniques like artificial neural networks (ANN). Neural networks can learn from complex datasets and model intricate relationships, making them promise for accurate predictions. By analyzing engine parameters such as fuel type, air-fuel ratio, speed, load, and temperature, neural networks can identify patterns influencing emission levels. These models enable engineers to optimize efficiency and reduce harmful emissions. ANN offers advantages in predicting efficiency by learning from vast amounts of data, extracting meaningful patterns, and identifying complex relationships. Accurate predictions result in better performance, fuel economy, and reduced environmental impacts. Studies have successfully employed ANN to estimate engine emissions and performance, showcasing its reliability in predicting engine characteristics. By leveraging ANN, informed decisions can be made regarding engine design, adjustments, and optimization techniques, leading to enhanced fuel efficiency and reduced emissions. Predicting engine efficiency using ANN holds promise for achieving environmental sustainability in the automotive sector.
Keywords
Engine efficiency prediction Environmental sustainability Fuel efficiency enhancement Artificial neural networks (ANN) Emission reduction
References
- S. Uslu and M. B. Celik, “Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether,” Engineering Science and Technology, an International Journal, vol. 21, no. 6, pp. 1194–1201, Dec. 2018, doi: 10.1016/J.JESTCH.2018.08.017.
- J. Fu et al., “Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine,” Appl Therm Eng, vol. 201, p. 117749, Jan. 2022, doi: 10.1016/J.APPLTHERMALENG.2021.117749.
- H. Oǧuz, I. Saritas, and H. E. Baydan, “Prediction of diesel engine performance using biofuels with artificial neural network,” Expert Syst Appl, vol. 37, no. 9, pp. 6579–6586, Sep. 2010, doi: 10.1016/J.ESWA.2010.02.128.
- Y. Çay, A. Çiçek, F. Kara, and S. Saǧiroǧlu, “Prediction of engine performance for an alternative fuel using artificial neural network,” Appl Therm Eng, vol. 37, pp. 217–225, May 2012, doi: 10.1016/J.APPLTHERMALENG.2011.11.019.
- 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,” Renew Energy, vol. 34, no. 4, pp. 976–982, Apr. 2009, doi: 10.1016/J.RENENE.2008.08.008.
- Y. Cay, “Prediction of a gasoline engine performance with artificial neural network,” Fuel, vol. 111, pp. 324–331, Sep. 2013, doi: 10.1016/J.FUEL.2012.12.040.
- Y. Çay, I. Korkmaz, A. Çiçek, and F. Kara, “Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network,” Energy, vol. 50, no. 1, pp. 177–186, Feb. 2013, doi: 10.1016/J.ENERGY.2012.10.052.
- M. I. Arbab, H. H. Masjuki, M. Varman, M. A. Kalam, S. Imtenan, and H. Sajjad, “Fuel properties, engine performance and emission characteristic of common biodiesels as a renewable and sustainable source of fuel,” Renewable and Sustainable Energy Reviews, vol. 22, pp. 133–147, Jun. 2013, doi: 10.1016/J.RSER.2013.01.046.
- R. K. Rai and R. R. Sahoo, “Engine performance, emission, and sustainability analysis with diesel fuel-based Shorea robusta methyl ester biodiesel blends,” Fuel, vol. 292, p. 120234, May 2021, doi: 10.1016/J.FUEL.2021.120234.
- A. Tuan Hoang et al., “A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels,” Sustainable Energy Technologies and Assessments, vol. 47, p. 101416, Oct. 2021, doi: 10.1016/J.SETA.2021.101416.
- URL-1, “The Basics of Neural Networks (Neural Network Series) — Part 1 | by Kiprono Elijah Koech | Towards Data Science.” https://towardsdatascience.com/the-basics-of-neural-networks-neural-network-series-part-1-4419e343b2b (accessed May 29, 2023).
- H. Kahraman, İ. Cesur, B. Eren, and A. Çoban, “Biyodizel Yakıt Kullanan İçten Yanmalı Motorlarda Aşınma-Sürtünme Optimizasyonu ve Tahmini için Taguchi ve Yapay Sinir Ağı Uygulaması,” Politeknik Dergisi, pp. 1–1, Dec. 2023, doi: 10.2339/POLITEKNIK.1216411.
- B. Oyar, B. Eren, and A. Özdemir, “Removal of Reactive Black 5 from Polluted Solutions by Electrocoagulation: Modelling Experimental Data Using Artificial Neural Networks,” Sakarya University Journal of Science, vol. 24, no. 4, pp. 712–724, Aug. 2020, doi: 10.16984/SAUFENBILDER.698146.
- K. C. Yao, W. T. Huang, C. C. Wu, and T. Y. Chen, “Establishing an AI Model on Data Sensing and Prediction for Smart Home Environment Control Based on LabVIEW,” Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/7572818.
- S. Kılıçarslan and K. Adem, “An overview of the activation functions used in deep learning algorithms,” Journal of New Results in Science, vol. 10, no. 3, pp. 75–88, 2021, doi: 10.54187/jnrs.1011739.
- B. Eren, M. Yaqub, and V. Eyupoglu, “A comparative study of artificial neural network models for the predictioof Cd removal efficiency of polymer inclusion membranes,” Desalination Water Treat, vol. 143, 2019, doi: 10.5004/dwt.2019.23531.
Abstract
References
- S. Uslu and M. B. Celik, “Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether,” Engineering Science and Technology, an International Journal, vol. 21, no. 6, pp. 1194–1201, Dec. 2018, doi: 10.1016/J.JESTCH.2018.08.017.
- J. Fu et al., “Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine,” Appl Therm Eng, vol. 201, p. 117749, Jan. 2022, doi: 10.1016/J.APPLTHERMALENG.2021.117749.
- H. Oǧuz, I. Saritas, and H. E. Baydan, “Prediction of diesel engine performance using biofuels with artificial neural network,” Expert Syst Appl, vol. 37, no. 9, pp. 6579–6586, Sep. 2010, doi: 10.1016/J.ESWA.2010.02.128.
- Y. Çay, A. Çiçek, F. Kara, and S. Saǧiroǧlu, “Prediction of engine performance for an alternative fuel using artificial neural network,” Appl Therm Eng, vol. 37, pp. 217–225, May 2012, doi: 10.1016/J.APPLTHERMALENG.2011.11.019.
- 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,” Renew Energy, vol. 34, no. 4, pp. 976–982, Apr. 2009, doi: 10.1016/J.RENENE.2008.08.008.
- Y. Cay, “Prediction of a gasoline engine performance with artificial neural network,” Fuel, vol. 111, pp. 324–331, Sep. 2013, doi: 10.1016/J.FUEL.2012.12.040.
- Y. Çay, I. Korkmaz, A. Çiçek, and F. Kara, “Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network,” Energy, vol. 50, no. 1, pp. 177–186, Feb. 2013, doi: 10.1016/J.ENERGY.2012.10.052.
- M. I. Arbab, H. H. Masjuki, M. Varman, M. A. Kalam, S. Imtenan, and H. Sajjad, “Fuel properties, engine performance and emission characteristic of common biodiesels as a renewable and sustainable source of fuel,” Renewable and Sustainable Energy Reviews, vol. 22, pp. 133–147, Jun. 2013, doi: 10.1016/J.RSER.2013.01.046.
- R. K. Rai and R. R. Sahoo, “Engine performance, emission, and sustainability analysis with diesel fuel-based Shorea robusta methyl ester biodiesel blends,” Fuel, vol. 292, p. 120234, May 2021, doi: 10.1016/J.FUEL.2021.120234.
- A. Tuan Hoang et al., “A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels,” Sustainable Energy Technologies and Assessments, vol. 47, p. 101416, Oct. 2021, doi: 10.1016/J.SETA.2021.101416.
- URL-1, “The Basics of Neural Networks (Neural Network Series) — Part 1 | by Kiprono Elijah Koech | Towards Data Science.” https://towardsdatascience.com/the-basics-of-neural-networks-neural-network-series-part-1-4419e343b2b (accessed May 29, 2023).
- H. Kahraman, İ. Cesur, B. Eren, and A. Çoban, “Biyodizel Yakıt Kullanan İçten Yanmalı Motorlarda Aşınma-Sürtünme Optimizasyonu ve Tahmini için Taguchi ve Yapay Sinir Ağı Uygulaması,” Politeknik Dergisi, pp. 1–1, Dec. 2023, doi: 10.2339/POLITEKNIK.1216411.
- B. Oyar, B. Eren, and A. Özdemir, “Removal of Reactive Black 5 from Polluted Solutions by Electrocoagulation: Modelling Experimental Data Using Artificial Neural Networks,” Sakarya University Journal of Science, vol. 24, no. 4, pp. 712–724, Aug. 2020, doi: 10.16984/SAUFENBILDER.698146.
- K. C. Yao, W. T. Huang, C. C. Wu, and T. Y. Chen, “Establishing an AI Model on Data Sensing and Prediction for Smart Home Environment Control Based on LabVIEW,” Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/7572818.
- S. Kılıçarslan and K. Adem, “An overview of the activation functions used in deep learning algorithms,” Journal of New Results in Science, vol. 10, no. 3, pp. 75–88, 2021, doi: 10.54187/jnrs.1011739.
- B. Eren, M. Yaqub, and V. Eyupoglu, “A comparative study of artificial neural network models for the predictioof Cd removal efficiency of polymer inclusion membranes,” Desalination Water Treat, vol. 143, 2019, doi: 10.5004/dwt.2019.23531.
Details
| Primary Language | English |
|---|---|
| Subjects | Environmentally Sustainable Engineering |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | August 27, 2023 |
| Publication Date | August 31, 2023 |
| Submission Date | June 8, 2023 |
| Acceptance Date | July 17, 2023 |
| Published in Issue | Year 2023Volume: 6 Issue: 2 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License