JOURNAL ARTICLE

Environmental and performance impacts of 2-ethylhexyl nitrate and ethanol in diesel blends: A comprehensive study.

  • Published In: Journal of Renewable & Sustainable Energy, 2024, v. 16, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Salmani, Mahir Husain; Hussain, Inayat; Rehman, Sanaur; Kumar, Himansh 3 of 3

Abstract

This article investigates the performance and emissions of a compression ignition (CI) engine fueled with ethanol-diesel blends enhanced by 2-ethylhexyl nitrate (2-EHN) as a cetane improver. Three fuel blends—E20 (20% ethanol, 80% diesel), E20A (20% ethanol, 0.1% 2-EHN, 79.9% diesel), and E20B (20% ethanol, 0.2% 2-EHN, 79.8% diesel)—were tested at a fixed compression ratio of 16.5 under varying load conditions, with results compared to pure petroleum diesel. The study found that E20B achieved higher brake thermal efficiency and indicated power at full load than diesel, but blends containing 2-EHN (E20A and E20B) exhibited increased emissions of hydrocarbons (HC), carbon dioxide (CO2), and nitrogen oxides (NOx) compared to diesel and E20. While ethanol blends reduced carbon monoxide (CO) emissions relative to diesel, the addition of 2-EHN negatively affected combustion characteristics and oxidative stability, indicating a need for further optimization to balance performance gains with emission control. The findings highlight the potential of ethanol-diesel blends as renewable alternatives but caution that cetane improvers like 2-EHN require careful evaluation to ensure sustainable engine operation.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2024/07, Vol. 16, Issue 4, p1
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2024
  • ISSN:1941-7012
  • DOI:10.1063/5.0199235
  • Accession Number:179373486
  • Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics 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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