Abstract
Changes in mill feed conditions such as particle size distribution and ore type have a significant impact on grinding performance, but are process variables that have remained largely unexploited in control and optimisation strategies. This study documents the development of a machine vision strategy used to characterise particle features in ore feed systems. In contrast to algorithm based methods which explicitly target individual particle recognition, this paper explores a textural approach, viz. variance and range textural operators for ore type characterisation and surface particle size estimation. Promising preliminary results were obtained for mean particle size predictions on an industrial mill feeder.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
