Online measurement of moisture content of biomass based on LiDAR images

Abstract

Fast real-time moisture content (MC) measurement of biomass feedstock is important for stable and efficient combustion control in biomass furnaces. In this study, a novel MC measurement method using LiDAR images was proposed based on the calibrated relationship between the signal echo intensity and sample (sawdust and straw) moisture. The experimental results showed that when the MC increased from 0% to 50%, the echo intensity decreased from 5% to 11%, and the relationship between the MC and echo intensity was well fitted by a linear or quadratic function. The influences of the measurement distance, composition, and temperature of the biomass on the measurement accuracy were explored. The absolute measurement error was <4%, and the maximum absolute error was 2.88% for pure sawdust. For the mixed sample (75% straw + 25% sawdust), the maximum absolute error was 3.60%. The optimal measurement distance between the sample and LiDAR was 64 cm, and the maximum absolute error was 3.27%. The influence of temperature between 20 °C and 50 °C on the measurement accuracy was negligible and the maximum absolute error was 3.23% at 30 °C. The verification results confirmed that the proposed LiDAR method can accurately perform online MC measurements.