Monitoring, modelling and simulation of spiral concentrators

Spiral concentrators are robust gravity separation devices often compactly implemented in industry with large amounts of spirals per plant – organized in banks. Current automated monitoring strategies at spiral concentrator plants involve quantifying overall feed and product stream states. However, spiral unit monitoring is performed by manual operator inspection and control is mainly achieved by operators manually changing splitter settings of spirals across a plant. In large spiral plants, containing thousands of individual spiral concentrators, changing splitters can become tedious or is sometimes neglected. Automated monitoring and control of spirals can aid spiral plant operators in achieving optimal spiral plant performance. Computer vision orientated mineral interface detection have been proposed, in past studies, as a method to monitor spiral concentrators. This is due to the formation of different mineral bands within spiral troughs during heavy mineral separation. Particles differentiate based on density and size differences usually creating three, visually discernible, mineral bands (flowing down the spiral trough). These streams are known as the concentrate, middling and tailings streams. The concentrate band is often visually darker than the streams containing gangue and the mineral interfaces can serve as a useful cue for setting splitters. However, interface tracking on industrial slurries have not yet been demonstrated and due to the large number of spirals within spiral plants it is necessary to determine what sparse sensor implementation will look like (this is due to the lack of appropriate sensor placement algorithms for metallurgical plants). This text follows a framework that spans from sensor development to sensor implementation strategy within spiral concentration plants – exploring possible stumbling blocks along the way. A spiral interface sensor is proposed, as a spiral monitoring tool, and demonstrated with experimental work during which spiral modelling was also performed. Two image processing algorithms, CVI (edge detection based) and CVII (logistic regression based), were prepared to detect spiral interfaces. Experimental modelling of a Multotec SC21 spiral concentrator was performed by formulating and comparing response surface methodology (RSM) with a proposed extended Holland-Batt model. Two sensor placement strategies, SPI (state estimation based) and SPII (metallurgical performance based), were prepared to help determine important monitoring positions based on steady state spiral plant simulations. Optimal monitoring locations minimize sensor network financial cost while maximizing some proxy for monitoring benefit. Spiral concentrator and spiral plant modelling (including optimal sensor placement) is based on the case study of the Glencore Rowland spiral plant which treats slurry containing UG2 ores to upgrade chromite content. Algorithm CVII proved to be the superior interface detection approach and can identify chromite concentrate interfaces in slurry representative of industrial conditions. Spiral splitter control should be further investigated; however, spiral unit monitoring will still provide operators with useful information on process changes (should control be infeasible or unprofitable). RSM models were more precise than the extended Holland-Batt model; however, the latter showed superior extrapolation Stellenbosch University https://scholar.sun.ac.za