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Development of Key-Enabling Technologies for a Variable-blend Natural Gas Vehicle

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  • Park, Chan Seung
  • Roy, Partho

Abstract

A portable, economic and reliable sensor for the Natural Gas (NG) fuel quality has been developed. Both Wobbe Index (WI) and Methane Indexes (MI) as well as inert gas content (inert%) of the NG fuel can be measured in real time within 5% accuracy. This sensor is targeting to be used in any equipment that involves NG combustion including NG vehicle, boiler, building HVAC, various consumer level gas appliance and Variable Natural Gas Vehicle (VNGV). The VNGV is an NG vehicle that can operate on any arbitrary mixture of CH4 and CO2, thus allowing the use of Renewable Natural Gas (RNG) including biogas for transportation without comprehensive gas cleanup/upgrading. The technology behind is to predict the “Value of Interests” (WI, MI and inert%) by the signals from easily “Measurable Physical Properties” (such as thermal conductivity, temperature, etc..), as shown in the figure. Prediction of “Value of Interest” by data mining (esp. Multivariate Analysis and/or Artificial Neural Network) is the key idea of the concept. This technology is non-invasive, rugged, and small in size promising to overcome limitations and shortcomings such as bulky size and intrusive nature of conventional measurement technology. VNGV technology will enable widespread use of RNG as a transportation fuel, resulting in significant reductions in GHG emissions in the transportation sector. View the NCST Project Webpage

Suggested Citation

  • Park, Chan Seung & Roy, Partho, 2017. "Development of Key-Enabling Technologies for a Variable-blend Natural Gas Vehicle," Institute of Transportation Studies, Working Paper Series qt2927d8hr, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt2927d8hr
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    References listed on IDEAS

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    1. Kakaee, Amir-Hasan & Paykani, Amin & Ghajar, Mostafa, 2014. "The influence of fuel composition on the combustion and emission characteristics of natural gas fueled engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 64-78.
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    More about this item

    Keywords

    Engineering; Algorithms; Biomass fuels; Databases; Fuel mixtures; Fuel systems; In vehicle sensors; Natural gas vehicles; Renewable energy sources; Simulation;
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