A simple image-based method for online moisture content estimation of iron ore green pellets
Shu-yi Zhou1, Xiao-yan Liu1,2
1 College of Electrical and Information Engineering, Hunan University, Changsha 410082, Hunan, China 2 Innovation Institute of Industrial Design and Machine Intelligence Quanzhou-Hunan University, Quanzhou 362006, Fujian, China
A simple image-based method for online moisture content estimation of iron ore green pellets
Shu-yi Zhou1, Xiao-yan Liu1,2
1 College of Electrical and Information Engineering, Hunan University, Changsha 410082, Hunan, China 2 Innovation Institute of Industrial Design and Machine Intelligence Quanzhou-Hunan University, Quanzhou 362006, Fujian, China
摘要 Moisture content (MC) is an important quality metric of iron ore green pellets in pelletizing process in ironmaking industry. Current image-based methods for MC estimation may result in big errors for gray-scale pellet images captured under various lighting conditions. We proposed a simple image-based method to improve the MC estimation accuracy by illumination correction and linear regression modeling. Firstly, the illumination of the pellet image was transformed into the reference illumination by use of a color checker chart and piecewise linear interpolation, so that the influence of different illuminations could be greatly reduced. By experimental analysis, it was found that MC is approximately adversely proportional to the average intensity of the transformed images. A simple model for MC prediction was then established by linear fitting. Experiments demonstrated that the proposed method has good robustness to different lighting conditions and achieves the best performance in metrics of mean square error, mean absolute error and maximum absolute error in comparison with five state-of-art MC estimating methods. Application on a working disk pelletizer shows that the proposed method can predict well the change of moisture content with time, and its computing efficiency can satisfy the requirement for online MC monitoring during the pelletizing process.
Abstract：Moisture content (MC) is an important quality metric of iron ore green pellets in pelletizing process in ironmaking industry. Current image-based methods for MC estimation may result in big errors for gray-scale pellet images captured under various lighting conditions. We proposed a simple image-based method to improve the MC estimation accuracy by illumination correction and linear regression modeling. Firstly, the illumination of the pellet image was transformed into the reference illumination by use of a color checker chart and piecewise linear interpolation, so that the influence of different illuminations could be greatly reduced. By experimental analysis, it was found that MC is approximately adversely proportional to the average intensity of the transformed images. A simple model for MC prediction was then established by linear fitting. Experiments demonstrated that the proposed method has good robustness to different lighting conditions and achieves the best performance in metrics of mean square error, mean absolute error and maximum absolute error in comparison with five state-of-art MC estimating methods. Application on a working disk pelletizer shows that the proposed method can predict well the change of moisture content with time, and its computing efficiency can satisfy the requirement for online MC monitoring during the pelletizing process.
Shu-yi Zhou,Xiao-yan Liu. A simple image-based method for online moisture content estimation of iron ore green pellets[J]. Journal of Iron and Steel Research International, 2023, 30(05): 851-863.
2023, Vol. 30 
