Statistical Analyses of CNG Constituents on Dual-Fuel Compression Ignition Combustion

2016-01-0802

04/05/2016

Event
SAE 2016 World Congress and Exhibition
Authors Abstract
Content
The use of Compressed Natural Gas (CNG) has demonstrated the potential to decrease Particulate Matter (PM) and nitrogen oxide (NOx) emissions simultaneously when used in a dual-fuel application with diesel fuel functioning as the ignition source. However, some authors do find that NOx emissions can increase. One postulation is that the conflicting results in the literature may be due to the difference in composition of natural gas around the world. Therefore, in order to investigate if CNG composition influences combustion performance and emissions, four unique mixtures of CNG were tested (i.e., 87% to 96% methane) while minimizing the combined difference of the density, heating value, and constant pressure specific heat of each mixture. This was accomplished at moderate energy substitution ratios (up to 40%) in a single cylinder engine operating at various loads. Previous analysis of these results did not reveal noticeable macroscopic trends with respect to the relative amounts of methane, ethane, propane, and isobutane. Thus, a statistical analysis using 2-way and 1-way Analysis of Variance along with Pearson correlations was performed to determine if dependencies exist between the results and the composition of CNG. It was found that the loading and substitution rate dominated the results with a relatively small influence noted from the amount of methane on the hydrocarbon emissions. As a result, the CNG composition normalization procedure employed may provide a simple methodology of ensuring consistent performance and emission results from different CNG compositions when used in a dual-fuel manner with diesel.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-0802
Pages
10
Citation
Langness, C., and Depcik, C., "Statistical Analyses of CNG Constituents on Dual-Fuel Compression Ignition Combustion," SAE Technical Paper 2016-01-0802, 2016, https://doi.org/10.4271/2016-01-0802.
Additional Details
Publisher
Published
Apr 5, 2016
Product Code
2016-01-0802
Content Type
Technical Paper
Language
English