AI Summary & Key Takeaways
AI InsightThe velocity measurement and the operation guidelines for the flotation operation and automatic control lay a foundation for the establishment of a flotation optimal control system based on machine vision monitoring.
📌 3 Key Takeaways
Abstract
The flow velocity of flotation froth involves important information about the concentrate grade and the mineral recovery. It is significant to maintain the flow velocity of the froth at proper levels to achieve a good production performance in the machine vision-based process monitoring and control. However, the accurate velocity field measurement for the heavily deformed and seriously fragile froth bubbles is still a great challenge. Scale-invariant feature transform (SIFT) feature-based image matching provides an effective registration method for deformed objects, but most of the processing time would be wasted on the feature extraction and matching in irrelevant regions of the image pairs, which is unsuitable for online process monitoring. The Kalman filter is an effective tool to predict the next positions of the subblocks in the froth image sequences. Taking advantage of the merits of the SIFT and the Kalman filtering, an online froth velocity field measurement method based on the SIFT features and an improved Kalman filtering is presented in this paper. This method can obtain the accurate velocity field of various kinds of froth bubbles flowing to the scraper in the flotation cells, even including the bubbles with serious collapse and heavy deformation. The accuracy of the subblock registration is evaluated by a deformation model. After a long period of observation in a bauxite flotation plant, the relationship between the froth flow velocity and the production performance indices is analyzed and discussed. Finally, a kind of conclusive operation advice based on the relationship between the froth velocity and the flotation performance is established. The velocity measurement and the operation guidelines for the flotation operation and automatic control lay a foundation for the establishment of a flotation optimal control system based on machine vision monitoring.
Özet
The flow velocity of flotation froth involves important information about the concentrate grade and the mineral recovery. It is significant to maintain the flow velocity of the froth at proper levels to achieve a good production performance in the machine vision-based process monitoring and control. However, the accurate velocity field measurement for the heavily deformed and seriously fragile froth bubbles is still a great challenge. Scale-invariant feature transform (SIFT) feature-based image matching provides an effective registration method for deformed objects, but most of the processing time would be wasted on the feature extraction and matching in irrelevant regions of the image pairs, which is unsuitable for online process monitoring. The Kalman filter is an effective tool to predict the next positions of the subblocks in the froth image sequences. Taking advantage of the merits of the SIFT and the Kalman filtering, an online froth velocity field measurement method based on the SIFT features and an improved Kalman filtering is presented in this paper. This method can obtain the accurate velocity field of various kinds of froth bubbles flowing to the scraper in the flotation cells, even including the bubbles with serious collapse and heavy deformation. The accuracy of the subblock registration is evaluated by a deformation model. After a long period of observation in a bauxite flotation plant, the relationship between the froth flow velocity and the production performance indices is analyzed and discussed. Finally, a kind of conclusive operation advice based on the relationship between the froth velocity and the flotation performance is established. The velocity measurement and the operation guidelines for the flotation operation and automatic control lay a foundation for the establishment of a flotation optimal control system based on machine vision monitoring.