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
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Abstract
References
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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 2023 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License