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Methodology for Formulating Diesel Surrogate Fuels with Accurate Compositional, Ignition-Quality, and Volatility Characteristics



Charles J. Mueller, William J. Cannella, Thomas J. Bruno, Bruce Bunting, Heather Dettman, James A. Franz, Marcia L. Huber, Mani Natarajan, William J. Pitz, Matthew A. Ratcliff, Ken Wright


In this work a novel approach is developed to formulate surrogate fuels having characteristics that are representative of diesel fuels produced from real-world refinery streams. Because diesel fuels consist of hundreds of compounds, it is difficult to conclusively determine the effects of fuel composition on combustion properties. Surrogate fuels, being simpler representations of these practical fuels, are of interest because they can provide a better understanding of fundamental fuel-composition and property effects on combustion and emissions-formation processes in internal-combustion engines. In addition, the application of surrogate fuels in numerical simulations with accurate vaporization, mixing, and combustion models could revolutionize future engine designs by enabling computational optimization for evolving real fuels. Dependable computational design would not only improve engine function, it would do so at significant cost savings relative to current optimization strategies that rely on physical testing of hardware prototypes. The approach in this study utilizes state-of-the-art techniques of 13C and 1H nuclear magnetic resonance spectroscopy and the advanced distillation curve to characterize the composition and the volatility, respectively, of a target real diesel fuel. The ignition quality is quantified using the derived cetane number. Two well-characterized, ultra-low-sulfur #2 diesel reference fuels were used as target fuels: a 2007 emissions certification fuel and a Coordinating Research Council (CRC) Fuels for Advanced Combustion Engines (FACE) diesel fuel. A set of nine pure compounds was selected to create the surrogates. Known carbon bond types, as well as models for ignition quality and volatility, were used in a multi-property regression algorithm to determine optimal surrogate formulations. The surrogates were blended, and their measured and predicted properties are compared to the measured properties of the target fuels.
Energy and Fuels


advanced distillation curve, cetane number, diesel, NMR, regression model, surrogate fuel


Mueller, C. , Cannella, W. , Bruno, T. , Bunting, B. , Dettman, H. , Franz, J. , Huber, M. , Natarajan, M. , Pitz, W. , Ratcliff, M. and Wright, K. (2012), Methodology for Formulating Diesel Surrogate Fuels with Accurate Compositional, Ignition-Quality, and Volatility Characteristics, Energy and Fuels, [online], (Accessed May 26, 2024)


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Created May 13, 2012, Updated October 12, 2021