A215 THE MOISTURE ESTIMATION OF BIOMASS FUEL : A NEURAL NETWORK BASED METHOD

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  • Yu Yuefeng
    School of Mechanical and Power Engineering, Shanghai Jiao Tong University
  • Berger Roland
    Institute fur Verfahrenstechnik und Dampfkesselwesen (IVD), Stuttgart University
  • Hein R. G. Klaus
    Institute fur Verfahrenstechnik und Dampfkesselwesen (IVD), Stuttgart University

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This paper focuses on the novel sensor system for measuring the moisture in biomass fuels, mainly in wood fuels, based on neural network models, which is low cost and a high performance. The sensor system for measuring the moisture in biomass fuels developed in this paper is quite different from the techniques before. In this new sensor system, the normal semiconductor humidity, pressure and temperature sensors will be installed in the fuel feeder in test facility, where the air humidity, pressure and temperature are measured. These signals will be fed together with operational data and ambient conditions in neural network models that have been trained by the experiment data in advance to estimate the moisture in biomass fuels. The information generated by the Neural Network will be used for the dynamic control system.

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