Valta, A., Ruusunen, M., & Leiviskä, K. (2020). On-line moisture content estimation of saw dust via machine vision. Open Engineering, 10(1), 336–349. https://doi.org/10.1515/eng-2020-0035
On-line moisture content estimation of saw dust via machine vision
|Author:||Valta, Art1; Ruusunen, Mika1; Leiviskä, Kauko1|
1Control Engineering, Environmental and Chemical Engineering, University of Oulu, P.O. Box 4300, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020070747069
|Publish Date:|| 2020-07-07
The effect of moisture content and feasibility of its estimation in granular material was investigated via machine vision. The test scheme consisted of saw dust samples derived from Norway spruce with moisture content adjusted to three distinct levels. The effect of moisture when present as ice or liquid water was compared. The experimental procedure consisted of pouring the saw dust under video camera recording. The equipment setup consisted of a vibrator feeder and custom-built pouring frame. Still images were extracted with fixed sample time from the recording done during the pouring procedure. From the extracted frames the dynamic behavior of cone profile was investigated via statistical means. It was observed that 2nd standardized moment correlated with moisture content, phase of water and their interaction. Furthermore, 4th standardized moment correlated with moisture content and phase. The 3rd moment was inspected qualitatively from which it was observed that wet samples exhibited tendency to build mass accumulation sites with increasing moisture content. Samples where water was present as ice this was observed in a very small scale with all moisture content values. Corroborated by optical microscopy, these correlations were deduced to be due to liquid bridging in the bulk. Moisture content when present as ice was, however, observed to have a drastic effect on the overall cone shape. Based on these findings, a machine vision application could be feasible way to estimate moisture content on-line in thawed saw dust by using statistical parameters in classification decision making. This would enable cost-effective on-line monitoring of moisture content and a control circuit to be designed.
|Pages:||336 - 349|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
215 Chemical engineering
© 2020 A. Valta et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 Public License. BY 4.0.