Engine manufacturers and researchers in the United States are finding growing interest among customers in the use of opportunity fuels such as syngas from the gasification and pyrolysis of biomass and biogas from anaerobic digestion of biomass. Once adequately cleaned, the most challenging issue in utilizing these opportunity fuels in engines is that their compositions can vary from site to site and with time depending on feedstock and process parameters. At present, there are no identified methods that can measure the composition and heating value in real-time. Key fuel properties of interest to the engine designer/researcher such as heating value, laminar flame speed, stoichiometric air to fuel ratio and Methane Number can then be determined. This paper reports on research aimed at developing a real-time method for determining the composition of a variety of opportunity fuels and blends with natural gas. Interfering signals from multiple measurement sources are processed collectively using multivariate regression methods, such as, the principal components regression and partial least squares regression to predict the composition and energy content of the fuel blends. The accuracy of the method is comparable to gas chromatography.

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