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Towards Model-Based Identification of Biofuels for Compression Ignition Engines

Journal Article
ISSN: 1946-3952, e-ISSN: 1946-3960
Published September 10, 2012 by SAE International in United States
Towards Model-Based Identification of Biofuels for Compression Ignition Engines
Citation: Dahmen, M., Hechinger, M., Victoria Villeda, J., and Marquardt, W., "Towards Model-Based Identification of Biofuels for Compression Ignition Engines," SAE Int. J. Fuels Lubr. 5(3):990-1003, 2012,
Language: English


Depleting fossil resources, a constantly rising energy demand and worries about greenhouse gas emissions force society to explore novel concepts for future mobile propulsion. In the context of biofuels, the identification of novel, sustainably producible, tailored molecules meeting the property specifications derived from advanced engine combustion concepts therefore constitutes a major objective. Due to the tremendous amount of possible molecular structures, solely experimental search strategies are infeasible and highlight the need for a computer-aided biofuel identification framework. To this end, a holistic approach for deriving truly predictive Quantitative-Structure-Property-Relationships (QSPRs) for engine-relevant fuel properties is presented. Such QSPRs are combined with a rigorous generation of molecular structures aiming at identification of single-compound fuel candidates for use in compression ignition (CI) engines. Besides addressing thermophysical fuel properties relevant to CI engines, an accurate QSPR model for the cetane number has been established and integrated into the fuel identification framework. A comprehensive list of potential CI fuel molecules is generated automatically and subsequently screened by sequentially applying property constraints to identify those compounds predicted to exhibit all desired physicochemical properties. In conjunction with the rigorous generation of structures, the outlined holistic QSPR methodology thus represents a feasible strategy for rapid computer-aided identification of fuel candidates tailored for optimal engine performance.