Online monitoring and control of froth flotation systems with machine vision:A review
MetadataShow full item record
Research and development into the application of machine vision in froth flotation systems has continued since its introduction in the late 1980s. Machine vision is able to accurately and rapidly extract froth characteristics, both physical (e.g. bubble size) and dynamic (froth velocity) in nature, from digital images and present these results to operators and/or use the results as inputs to process control systems. Currently, machine vision has been implemented on several industrial sites worldwide and the technology continues to benefit from advances in computer technology. Effort continues to be directed into linking concentrate grade with measurable attributes of the froth phase, although this is proving difficult. As a result other extracted variables, such as froth velocity, have to be used to infer process performance. However, despite more than 20 years of development, a long-term, fully automated control system using machine vision is yet to materialise. In this review, the various methods of data extraction from images are investigated and the associated challenges facing each method discussed. This is followed by a look at how machine vision has been implemented into process control structures and a review of some of the commercial froth imaging systems currently available. Lastly, the review assesses future trends and draws several conclusions on the current status of machine vision technology.
Showing items related by title, author, creator and subject.
The estimation of platinum flotation grade from froth image features by using artificial neural networksMarais, C.; Aldrich, Chris (2011)The use of machine vision in the monitoring and control of metallurgical plants has become a very attractive option in the last decade, especially since computing power has increased drastically inthe last few years. The ...
Aldrich, Chris; Kistner, M.; Jemwa, G. (2013)In the last few decades, developments in machine vision technology have led to innovative approaches to the control and monitoring of mineral processing systems. Image representation plays an important role in the performance ...
Fu, Y.; Aldrich, Chris (2018)Deep learning constitutes a significant recent advance in machine learning and has been particularly successful in applications related to image processing, where it can already surpass human accuracy in some cases. In ...