Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach

Beytullah Eren; İdris Cesur

doi:10.35377/saucis...1311014

EN

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

References

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Details

Primary Language

English

Subjects

Environmentally Sustainable Engineering

Journal Section

Research Article

Authors

Beytullah Eren ^*
0000-0001-6747-7004
Türkiye

İdris Cesur
0000-0001-7487-5676
Türkiye

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 2023 Volume: 6 Number: 2

DOI

https://doi.org/10.35377/saucis...1311014

IZ

https://izlik.org/JA54UU86WS

Cite

RIS / Bibtex

APA

Eren, B., & Cesur, İ. (2023). Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach. Sakarya University Journal of Computer and Information Sciences, 6(2), 105-113. https://doi.org/10.35377/saucis...1311014

AMA

1.Eren B, Cesur İ. Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach. SAUCIS. 2023;6(2):105-113. doi:10.35377/saucis.1311014

Chicago

Eren, Beytullah, and İdris Cesur. 2023. “Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach”. Sakarya University Journal of Computer and Information Sciences 6 (2): 105-13. https://doi.org/10.35377/saucis. 1311014.

EndNote

Eren B, Cesur İ (August 1, 2023) Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach. Sakarya University Journal of Computer and Information Sciences 6 2 105–113.

IEEE

[1]B. Eren and İ. Cesur, “Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach”, SAUCIS, vol. 6, no. 2, pp. 105–113, Aug. 2023, doi: 10.35377/saucis...1311014.

ISNAD

Eren, Beytullah - Cesur, İdris. “Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach”. Sakarya University Journal of Computer and Information Sciences 6/2 (August 1, 2023): 105-113. https://doi.org/10.35377/saucis. 1311014.

JAMA

1.Eren B, Cesur İ. Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach. SAUCIS. 2023;6:105–113.

MLA

Eren, Beytullah, and İdris Cesur. “Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 2, Aug. 2023, pp. 105-13, doi:10.35377/saucis. 1311014.

Vancouver

1.Beytullah Eren, İdris Cesur. Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach. SAUCIS. 2023 Aug. 1;6(2):105-13. doi:10.35377/saucis. 1311014