Optimization of gasoline engine operating parameters fueled with DIPE-gasoline blend: Comparative evaluation between response surface methodology and fuzzy logic expert system

Engine performance and emission characteristics of diisopropyl ether (DIPE)–gasoline blends were evaluated for several loads using a single-cylinder, four-stroke, multi-fuel variable compression ratio engine. The engine was operated with five test fuels: DIPE0 (neat gasoline), DIPE10 (90% gasoline +...

Full description

Saved in:
Bibliographic Details
Published in:Process safety and environmental protection 2022-02, Vol.158, p.291-307
Main Authors: Sivakumar, D.B., Arulmozhi, M., Sathyanarayanan, S., Sridharan, M.
Format: Article
Language:English
Subjects:
Alternate fuel
Brakes
Carbon monoxide
Compression
Correlation coefficient
Correlation coefficients
Emission analysis
emission control
Expert systems
Fuel consumption
Fuzzy logic
Fuzzy logic expert system
Gasoline
gasoline blend
gasoline engine
Gasoline engines
Nitrogen oxides
Optimization
Response surface methodology
Thermodynamic efficiency
Variable compression ratio
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Engine performance and emission characteristics of diisopropyl ether (DIPE)–gasoline blends were evaluated for several loads using a single-cylinder, four-stroke, multi-fuel variable compression ratio engine. The engine was operated with five test fuels: DIPE0 (neat gasoline), DIPE10 (90% gasoline + 10% DIPE), DIPE15 (85% gasoline + 15% DIPE), DIPE20 (80% gasoline + 20% DIPE), and DIPE25 (75% gasoline + 25% DIPE) by volume. The responses such as brake thermal efficiency (BTE), specific fuel consumption (SFC), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NOx) were optimized using response surface methodology (RSM) and fuzzy logic expert system (FLES) considering compression ratio (CR), brake power (BP), and DIPE blend percentage as input variables. The developed RSM model provided a significant fit with higher R2 (correlation coefficient) values. The RSM models showed better performance than the FLES model. The optimized responses such as BTE, SFC, CO, HC, and NOx were 26.9%, 0.378 kg/kW h, 0.0135% by volume, 152.66 ppm, and 3465 ppm, respectively. The optimized results were validated with the experimental results, and the error percentage for all the responses were low. Thus, the developed RSM models gave better results than FLES and can predict and optimize engine performance and emission characteristics.
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2021.12.015