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Simultaneous Moisture Content and Mass Flow Measurements in Wood Chip Flows Using Coupled Dielectric and Impact Sensors

机译:使用耦合介电和冲击传感器同时测量木屑流中的水分和质量流量

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摘要

An 8-electrode capacitance tomography (ECT) sensor was built and used to measure moisture content (MC) and mass flow of pine chip flows. The device was capable of directly measuring total water quantity in a sample but was sensitive to both dry matter and moisture, and therefore required a second measurement of mass flow to calculate MC. Two means of calculating the mass flow were used: the first being an impact sensor to measure total mass flow, and the second a volumetric approach based on measuring total area occupied by wood in images generated using the capacitance sensor’s tomographic mode. Tests were made on 109 groups of wood chips ranging in moisture content from 14% to 120% (dry basis) and wet weight of 280 to 1100 g. Sixty groups were randomly selected as a calibration set, and the remaining were used for validation of the sensor’s performance. For the combined capacitance/force transducer system, root mean square errors of prediction (RMSEP) for wet mass flow and moisture content were 13.42% and 16.61%, respectively. RMSEP using the combined volumetric mass flow/capacitance sensor for dry mass flow and moisture content were 22.89% and 24.16%, respectively. Either of the approaches was concluded to be feasible for prediction of moisture content in pine chip flows, but combining the impact and capacitance sensors was easier to implement. In situations where flows could not be impeded, however, the tomographic approach would likely be more useful.
机译:内置了8电极电容层析成像(ECT)传感器,用于测量水分含量(MC)和松木屑流的质量流量。该设备能够直接测量样品中的总水量,但对干物质和水分均敏感,因此需要对质量流量进行第二次测量才能计算出MC。使用了两种计算质量流量的方法:第一种是用于测量总质量流量的冲击传感器,第二种是基于体积的方法,该方法基于使用电容传感器的层析成像模式生成的图像中木材占据的总面积。对109组木片进行了测试,这些木片的含水量为14%至120%(干基),湿重为280至1100 g。随机选择60组作为校准集,其余的用于验证传感器的性能。对于组合式电容/力传感器系统,湿质量流量和水分含量的预测均方根误差(RMSEP)分别为13.42%和16.61%。使用组合体积质量流量/电容传感器的RMSEP的干质量流量和水分含量分别为22.89%和24.16%。结论中的任何一种方法都可用于预测松木屑流中的水分含量,但是将冲击传感器和电容传感器组合起来更易于实现。但是,在无法阻止流量的情况下,层析成像方法可能会更有用。

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