Multivariate statistics for summarizing diesel feeds for flammability attributes using comprehensive two-dimensional...

Area of Science:

• Petroleum Chemistry
• Analytical Chemistry
• Combustion Science

Background:

• Diesel fuel flammability is critical for engine performance and efficiency.
• Understanding hydrocarbon composition (paraffins, naphthenes, aromatics) is key to predicting flammability.
• Traditional methods for assessing flammability have limitations in complex mixtures.

Purpose of the Study:

• To evaluate the compositional variations of 19 diesel feeds.
• To correlate hydrocarbon composition with flammability properties.
• To apply multivariate statistical methods for feed characterization.

Main Methods:

• Comprehensive two-dimensional gas chromatography (GC×GC) for hydrocarbon analysis.
• Principal Component Analysis (PCA) for distinguishing feed clusters.
• Hierarchical Cluster Analysis (HCA) and heat maps for classifying paraffinic compositions.

Main Results:

• Diesel feeds exhibited wide compositional ranges: paraffins (9.6-57.8%), naphthenes (7.9-38.5%), and aromatics (10.5-82.3%).
• PCA successfully clustered feeds based on dominant hydrocarbon groups (paraffins, naphthenes, aromatics).
• HCA and heat maps differentiated feeds based on C9-C26 paraffin composition, particularly C15-C16 content.

Conclusions:

• Multivariate statistical methods effectively distinguish non-additive feed compositions influencing flammability.
• Hydrocarbon group composition significantly impacts diesel flammability through radical formation rates.
• GC×GC combined with chemometrics provides a powerful approach for diesel feed characterization.