JOURNAL ARTICLE
Optimizing the Factors Impacting on MPFI SI Engine Performance and Emissions Parameters using Taguchi Technique.
Published In: Journal of Mines, Metals & Fuels, 2025, v. 73, n. 6. P. 1749 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Deshpande, Ravindra S.; Gitay, Mayur J.; Magade, Pramod B.; Kardekar, Nitin; Diwate, Ajay D.; Patil, Pradip P.; Patil, Vijaya 3 of 3
Abstract
Alcohol-related fuels are gradually becoming the most suitable alternative fuel. The two alcohols that seem to have the greatest potential for use as fuel are ethanol and methanol. This was accomplished by running the test engine under various conditions, including varied ratios of alcohol to gasoline blends (A5, A20, and A25), compression ratios (10, 10.5, 11), and engine speeds (2100, 2800, and 3500 rpm). The Taguchi design of experiment (DoE) approach and one-way ANOVA were used to identify the optimal Factor Levels (FL) concerning performance (BP, BSFC, and BTE) as well as emissions (CO, CO2, HC, and NO) parameters. The trials were carried out using the combinations provided in the L9 orthogonal arrays (OA), which were found to be appropriate according to the design of the experiment. From the results, the best performance conditions are an engine speed (3500 rpm), a CR (11:1), and a blend ratio of 25% alcohol to gasoline (A25). The highest exhaust emissions from the blends were HC and CO2, while the lowest were NO and carbon monoxide. According to the analysis, engine performance and emission parameters were used to identify the optimal levels of each factor. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Mines, Metals & Fuels. 2025/06, Vol. 73, Issue 6, p1749
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2025
- ISSN:0022-2755
- DOI:10.18311/jmmf/2025/49023
- Accession Number:186297003
- Copyright Statement:Copyright of Journal of Mines, Metals & Fuels is the property of Books & Journal Private Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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