EPS eLibrary: NEURAL NETWORK MODELING OF THERMAL ENGINES POWERED BY ALTERNATIVE FUELS FOR THE PURPOSE OF REDUCING ATMOSPHERIC POLLUTION

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Title: NEURAL NETWORK MODELING OF THERMAL ENGINES POWERED BY ALTERNATIVE FUELS FOR THE PURPOSE OF REDUCING ATMOSPHERIC POLLUTION

Year

2024

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Peer Reviewed

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Title

NEURAL NETWORK MODELING OF THERMAL ENGINES POWERED BY ALTERNATIVE FUELS FOR THE PURPOSE OF REDUCING ATMOSPHERIC POLLUTION

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Authors

Constantin Georgescu; Valentin Amortila; Cristian Muntenita

DOI

10.5593/sgem2024/4.1/s19.55

DOI Issue

10.5593/sgem2024/4.1

ISSN

1314-2704

ISBN

978-619-7603-71-2

Language

English

Volume

Issue

4.1

Editor

• Prof. DSc. Oleksandr Trofymchuk, UKRAINE
• Prof. Dr. hab. oec. Baiba Rivza, LATVIA

Scientific Area

19.Air Pollution and Climate Change

Abstract/ Foreword

This research investigates the increasing use of alternative fuels in internal combustion engines, a phenomenon that has experienced significant growth in recent decades due to the heightened interest in reducing atmospheric pollution. Although the pollution level associated with alternative fuels is generally lower compared to fossil fuels, it is essential to emphasize that the pollutant impact of alcohol-based fuels depends on various factors, such as engine technology, mixture composition, fuel quality, and usage patterns. Thus, this study analyzes the influence of the alcohol proportion in gasoline on engine performance and, consequently, atmospheric pollution through an innovative optimization method. This method is based on the use of a neural modeling computer application, EasyNN, which generated a series of neural models with 1, 2, or 3 hidden layers. The data were obtained through tests performed on a four-stroke single-cylinder engine with a capacity of 582 cm3. Following the neural network modeling, it was concluded that the most advantageous combination is represented by an alternative fuel based on gasoline with a concentration of 6% methanol + 1.05% ethanol. In order to reduce the pollutant impact of vehicles, investigations in this field are ongoing, focusing on optimizing efficiency and reducing emissions associated with vehicles adopting alternative fuels.

References (click to open)

[1] M.Kapusuz, H.Ozcan, J.A. Yamin, “Research of performance on a spark ignition engine fueled by alcohol-gasoline blends using artificial neural networks,” Applied Thermal Engineering 91 (2015)
[2] Tony Sandberg, “Heavy Truck Modeling for Fuel Consumption,” Simulation an Measurements, Linkoping Studies in Science and Technology Thesis No. 924, Division of Vehicular Systems, Department of Electrical Engineering, Linkoping University, S–581 83 Link?oping, Sweden, http://www.vehicular.isy.liu.se/
[3] V. Amortila, “Controversy about car pollution: the electric vehicle or the classic vehicle?,” 19th SGEM Int. Multidiscip. Sci. GeoConference EXPO Proceedings19th, Energy Clean Technol., vol. 9, Dec. 2019.
[4] Z. Tian, X. Zhen, Y. Wang, D. Liu, and X. Li, “Comparative study on combustion and emission characteristics of methanol, ethanol and butanol fuel in TISI engine,” 2019.
[5] S. A. Shirazi, B. Abdollahipoor, B. Windom, K. F. Reardon, and T. D. Foust, “Effects of Blending C3-C4 Alcohols on Motor Gasoline Properties and Performance of Spark Ignition Engines: A Review,” 2019.
[6] J. Mueller, N. Kim, S. Lapointe, M. J. McNenly, M. Sjoberg, and R. Whitesides, “Optimization of fuel formulation using adaptive learning and artificial intelligence,” Artif. Intell. Data Driven Optim. Intern. Combust. Engines, pp. 27–45, Jan. 2022.
[7] Z. hao Ni, F. she Li, H. Wang, and H. Xiao, “Prediction of physical parameters of Jatropha biodiesel-ethanol dual fuel based on topological indices,” Appl. Energy, vol. 328, p. 120202, Dec. 2022.
[8] S.-H. Li et al., “Effects of Ethanol and Methanol on the Combustion Characteristics of Gasoline with the Revised Variation Disturbance Method,” vol. 7, 2022.

Acknowledgements (click to open)

This scientific work was carried out within the Internal Grant of the 2024 Department of Mechanical Engineering at the Faculty of Engineering of "Dunarea de Jos" University in Galati and the Centre for Interdisciplinary Research in Mechanical Engineering (CCIDIM).

Type

conference

Url

https://doi.org/10.5593/sgem2024/4.1/s19.55

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Proceedings Тitle

Proceedings of 24th International Multidisciplinary Scientific GeoConference SGEM 2024

Note

24th International Multidisciplinary Scientific GeoConference SGEM 2024, 1 - 7 July, 2024

Type

Proceedings Paper

Publisher

STEF92 Technology

Series

First click on Radio Buttons above - Scopus or Clarivate format

Organization

SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.

Pages

419-426

Date

1 - 7 July, 2024

Online Source Type

website

Digital Source Type

9768

Keywords

methanol, ethanol, atmospheric pollution, neural networks

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Title: NEURAL NETWORK MODELING OF THERMAL ENGINES POWERED BY ALTERNATIVE FUELS FOR THE PURPOSE OF REDUCING ATMOSPHERIC POLLUTION

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Acknowledgements (click to open)

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