Computational Analysis of HCCI Engine using Bioethanol-Diesel Blends as a Fuel

Bioethanol has long been recognized as a viable alternative fuel for diesel engines, offering significant potential to reduce emissions, particularly carbon monoxide (CO) and nitrogen oxides (NOx), when utilized in homogeneous charge compression ignition (HCCI) engines. Despite this promise, the production and optimization of bioethanol-diesel blends present considerable challenges, necessitating further investigation to address growing diesel fuel demands and stricter environmental regulations. This study explores the feasibility and impact of bioethanol-diesel blends on HCCI engine performance, focusing on combustion and emissions characteristics. Using Chemkin-PRO simulation software, blends with varying bioethanol concentrations (E10, E15, E20, and E30) are analyzed and compared against conventional n-heptane (diesel) fuel. The results demonstrate that increasing the bioethanol content in diesel blends leads to notable reductions in emissions. Specifically, NOx emissions were reduced by 52%, 59%, 69%, and 91% for E10, E15, E20, and E30, respectively. Similarly, CO emissions decreased by 19%, 26%, 30%, and 31% with the same blends. These findings highlight the potential of bioethanol-diesel blends to serve as environmentally sustainable alternatives to conventional diesel fuel, supporting efforts to mitigate emissions in HCCI engine applications.

Authors

Publication Details

Journal
Journal of Ship and Marine Structures
Published
2024-09-30
DOI
https://doi.org/10.37934/jsms.6.1.112
Primary Topic
Biodiesel Production and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computational Analysis of HCCI Engine using Bioethanol-Diesel Blends as a Fuel

Wan Nurdiyana Wan Mansor, Mohammad Nor Khasbi Jarkoni, N.A. Mohd Nasir, Sarah Alyssa Effendy
Journal of Ship and Marine Structures
Biodiesel Production and Applications
article

Computational Analysis of HCCI Engine using Bioethanol-Diesel Blends as a Fuel

Wan Nurdiyana Wan Mansor, Mohammad Nor Khasbi Jarkoni, N.A. Mohd Nasir, Sarah Alyssa Effendy
article en

Abstract

Bioethanol has long been recognized as a viable alternative fuel for diesel engines, offering significant potential to reduce emissions, particularly carbon monoxide (CO) and nitrogen oxides (NOx), when utilized in homogeneous charge compression ignition (HCCI) engines. Despite this promise, the production and optimization of bioethanol-diesel blends present considerable challenges, necessitating further investigation to address growing diesel fuel demands and stricter environmental regulations. This study explores the feasibility and impact of bioethanol-diesel blends on HCCI engine performance, focusing on combustion and emissions characteristics. Using Chemkin-PRO simulation software, blends with varying bioethanol concentrations (E10, E15, E20, and E30) are analyzed and compared against conventional n-heptane (diesel) fuel. The results demonstrate that increasing the bioethanol content in diesel blends leads to notable reductions in emissions. Specifically, NOx emissions were reduced by 52%, 59%, 69%, and 91% for E10, E15, E20, and E30, respectively. Similarly, CO emissions decreased by 19%, 26%, 30%, and 31% with the same blends. These findings highlight the potential of bioethanol-diesel blends to serve as environmentally sustainable alternatives to conventional diesel fuel, supporting efforts to mitigate emissions in HCCI engine applications.

Journal of Ship and Marine StructuresVol. 6(1)
Openalex Percentile: Top 75%
Biodiesel Production and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.