Enhancement of combustion characteristics of waste alcohol using n-heptane through RSM in an HCCI engine
Hüseyin Orhun Gürsoy,
Hamit Solmaz,
Tolga Kocakulak,
Turan Alp Arslan and
Alper Calam
Energy, 2024, vol. 313, issue C
Abstract:
The HCCI combustion mode, revealed as a result of internal combustion engine development studies, shortens the combustion duration and can operate at high compression ratios. Thanks to these advantages, it offers high thermal efficiency, high combustion efficiency, low NOx, and low soot emissions. In this study, an engine operating in HCCI combustion mode with fusel oil/n-heptane mixture fuel was optimized by RSM. The F30 fuel mixture used in the study consists of a volumetric combination of 30 % fusel oil-70 % n-heptane. The minimum and maximum values of the variable parameters are, an engine speed of 800–1400 rpm, a compression ratio of 1.9–2.7, and lambda 11–13, respectively. As a result of the optimization, optimum input parameters were calculated as 981 rpm engine speed, 1.9 lambda, and 11 compression ratio. The response parameters obtained depending on optimum input parameters are BSFC 261.628 g/kWh, IMEP 4.85 bar, ITE 31.69 %, HC 395.767 ppm, and CO 0.948 %. The importance and interactions of these input parameters were analyzed using the ANOVA method. As a result of the study, it has been seen that all multiple regression models developed with the RSM method can be used successfully to optimize engine performance and exhaust emissions.
Keywords: Waste alcohol; Combustion; Fusel oil; HCCI engine; Optimization; Response surface methodology (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224036168
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036168
DOI: 10.1016/j.energy.2024.133838
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().