TY - GEN
T1 - Measurement of the moisture content in woodchips through capacitive sensing and data driven modelling
AU - Yan, Jing
AU - Zhang, Wenbiao
AU - Yan, Yong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Woodchips as a common biomass are widely used in industrial processes and our daily lives. The moisture content in woodchips has a significant effect on the processing and combustion efficiency of woodchips. Therefore, it is necessary to develop a suitable method to measure the moisture content of woodchips. Capacitive sensors are widely used in various fields due to their simple structure, low cost and non-destruction to the samples. Because of the uniform sensitivity distribution compared to the parallel plate sensor design, a helical capacitive sensor is used in this paper to measure the moisture content of woodchips. The structure of the sensor is firstly optimized based on the orthogonal test method using a finite element model. Then, the influence of particle size and packing density of biomass on the capacitance readings is investigated. Finally, the moisture content is predicted through data driven modelling. Woodchip samples with different particle sizes, moisture content and packing densities are used to verify the model. It is found that the relative error between the predicted and reference values is within ±10% over the range of moisture contents from 7% to 49%.
AB - Woodchips as a common biomass are widely used in industrial processes and our daily lives. The moisture content in woodchips has a significant effect on the processing and combustion efficiency of woodchips. Therefore, it is necessary to develop a suitable method to measure the moisture content of woodchips. Capacitive sensors are widely used in various fields due to their simple structure, low cost and non-destruction to the samples. Because of the uniform sensitivity distribution compared to the parallel plate sensor design, a helical capacitive sensor is used in this paper to measure the moisture content of woodchips. The structure of the sensor is firstly optimized based on the orthogonal test method using a finite element model. Then, the influence of particle size and packing density of biomass on the capacitance readings is investigated. Finally, the moisture content is predicted through data driven modelling. Woodchip samples with different particle sizes, moisture content and packing densities are used to verify the model. It is found that the relative error between the predicted and reference values is within ±10% over the range of moisture contents from 7% to 49%.
KW - Data driven modeling
KW - Finite element model
KW - Helical capacitive sensor
KW - Index Terms -moisture content measurement
UR - https://www.scopus.com/pages/publications/85088318421
U2 - 10.1109/I2MTC43012.2020.9129280
DO - 10.1109/I2MTC43012.2020.9129280
M3 - 会议稿件
AN - SCOPUS:85088318421
T3 - I2MTC 2020 - International Instrumentation and Measurement Technology Conference, Proceedings
BT - I2MTC 2020 - International Instrumentation and Measurement Technology Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2020
Y2 - 25 May 2020 through 29 May 2020
ER -