Temperature measurement of stored biomass of different types and bulk densities using acoustic techniques

The internal temperature of stored biomass needs to be measured to suppress the possible self-ignition at biomass-fired power stations. Acoustic sensing has been proven to be a promising approach to measuring the temperature of stored wood pellets online and non-intrusively. In such a temperature measurement system, a characteristic factor is defined to derive the sound speed from measured time of flight of sound waves. The characteristic factor is updated based on its experimental relationship with the biomass temperature during temperature measurement. When the type, particle size, particle density and bulk density of stored biomass change, whether the relationship between the characteristic factor and biomass temperature needs to be recalibrated needs investigation. Therefore, the relationship between the characteristic factor and biomass property is modelled using the empirical equation of Miki. Then the model is used to analyse the impact of the particle size, particle density and bulk density of stored biomass on the relationship. An acoustic sensing system is constructed to investigate the influence of bulk density for different types of biomass. The system is also applied to measure the temperature of four biomass fuels, including wood blocks, wood pellets, wood chips, and wheat straws. Results show that the relative error of temperature measurements for the four types of biomass is within 3.5%, 5.7%, 6.8% and 2.5%, respectively, over the temperature range from 22.1 degrees C to 74.2 degrees C. The relationship between the characteristic factor and biomass temperature should be re-established for different types of biomass and different particle size distributions.