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.

Today, renewable fuels such as biogas are being used to fuel combined heat and power (CHP) and distributed generation (DG) systems. The composition of biogas delivered to power generation equipment varies depending upon the origin of the anaerobic digestion process and site-specific factors. For improved process control and optimum utilization of CHP/DG systems, the biogas composition needs to be monitored. A new apparatus has been developed for characterization of hydrocarbon fuel mixtures. The method utilizes near infrared absorption spectroscopy to monitor composition and heating value of landfill gas, natural gas, and other hydrocarbon fuel gases. The measurement is virtually instantaneous. A commercialized version of this sensor is expected to cost less than half the price of gas chromatographs, which are widely used in the gas industry today.

Today, producer gas is being utilized as a fuel gas in boilers, internal combustion engines and turbines for heat and power generation. The composition of producer gas varies depending upon the gasification parameters. For improved process control and optimum utilization of these heat and power generating systems, it is desirable to monitor the producer gas composition in real-time. A new method and apparatus has been developed and lab-tested for quantitative characterization of producer gas. Spectroscopic and non-spectroscopic measurements are performed in order to detect both — spectrally active and inactive gases. Both methods are cross-sensitive to more than one gas. The measurements are then processed using multivariate statistical methods — principal components regression and partial least squares to fit a regression model which correlates the experimental measurements to the composition and heating value of producer gas. The fitted regression model is used to estimate the properties of unknown mixtures. The measurements and data processing are done in real time using a high speed hardware control and data acquisition system. A commercialized version of this sensor is expected to cost less than half the price of gas chromatographs, which are widely used in the gas industry today.

No abstract

The authors are currently investigating new technical (both design and operation) approach, which is expected to enable the improvement of the performance of partially premixed type burners without jeopardizing the simplicity, cost, and reliability that this type of burners are well known for. The improvements include significant reduction of the NOx emission without substantial redesign of the combustion system. The results of the experimental investigation of burner operation and design improvements are to be presented and further discussed at the podium.