Measuring, Characterisation and Modelling of Load Dynamic Behaviour in a Wet Overflowdischarge Ball Mill
Year: 2012
Type: dissertation
Abstract: Overflow ball mills have found popular application in the ore dressing process for post-primary \ngrinding firstly owing to their ability to produce finer grinds, necessary for efficient mineral \nliberation and better flotation recovery and secondly due to lower initial capital outlay. However \nthey are inefficient and intensive energy consumers. This trend has been exacerbated in the wake \nof increased installation of large diameter ball mills to benefit from economies of scale, coupled \nwith diminishing ore quality currently being experienced by mines worldwide. To fully utilise \nthe available mill capacity and achieve optimal performance whilst maintaining energy \nefficiency for these large devices, closer and more effective control is needed. Satisfaction of this \nneed would result in stability of the entire mineral processing circuit, thereby reducing the \noverall cost in mineral extraction. Clear and deeper understanding of the in-mill behaviour is \nfundamental to the realisation of the above objective. \nThis thesis explores several experimental and modelling techniques to obtain deeper \nunderstanding of the internal behaviour of an overflow ball mill. A direct load sensor comprising \nan inductive proximity probe and a conductivity probe installed through the mill shell has been \nutilised to collect information of the media and slurry dynamic positions inside a laboratory ball \nmill while a commercial on-line ball and pulp sensor was employed to collect similar \ninformation on an industrial overflow ball mill. Useful insights were acquired that can help the \ndesign of control strategies for optimal mill performance. Four feature variables, i.e. dynamic \nmedia angle, slurry pool angle, conductivity signal amplitude and the slurry pool depth, derived \nfrom the sensor signals data were characteristically influenced by changes in mill operational \nconditions. Therefore the possibility of using these features to predict the associated mill \noperational variables is feasible. In view of the findings, two multivariate models, one based on \nthe concept of data projection to latent space (PLS) and the other combining PLS and radial basis \nfunctions neural networks (RBF) were built and applied to predict the in-mill slurry density and \nball load volume. Both models yielded adequate predictions, albeit the hybrid PLS-RBF model \ndisplayed marginally better prediction performance. The results are indicative of the available \npotential for mill on-line monitoring and control by multivariate techniques based on relevant \nfeatures contained in the media and slurry sensor signals data. \nIn another endeavour, a gamma camera was successfully employed to study the flow and mixing \nbehaviour of slurry inside a laboratory mill using Technetium-Tc99m radiotracer as a flow \nfollower. The effects of slurry viscosity and mill rotational speed on slurry mixing rate within the \nball charge and slurry exchange rate between the pool and the ball charge were assessed, yielding \ninsightful data. However, the results remain inconclusive as only qualitative information could \nbe obtained owing to the radiation attenuation effects by the steel ball charge. In the quest to improve the understanding of material transport inside the mill, the data acquired \non an industrial mill through salt tracer tests was adequately analysed to assess the variation of \nslurry residence time distribution (RTD) and volumetric holdup inside the mill as affected by \nchanges in slurry concentration and ball load volume. A model based on the concept of serial \nstirred mixers with a plug flow component produced fairly accurate predictions of the RTD data. \nAlso, equations derived from a mathematical description of the dynamic load profile produced \ngood estimates of the in-mill slurry volumetric holdup. \nFurther, an improved mixing-cell model was developed and applied to characterise the in-mill \nslurry hydrodynamic transport based on the measured RTD data. The model was able to account \nfor the effects of non-ideal flow conditions such as slurry back-mixing, slurry exchange between \nthe pool and ball charge and bypass flows on the main flow of slurry thus giving correct \ndescription of the inherent in-mill slurry transport dynamics. Note that failure to tune the mill \nappropriately to achieve desirable in-mill slurry transport behaviour may result in poor milling \nperformance and corresponding high energy expenditure. \nThus, the results obtained in this thesis clearly demonstrate that, a combination of experimental \ntechniques and mathematical models is a viable route to enhance understanding of mill internal \nbehaviour, which in turn enables development of better control schemes for optimal mill \nperformance.
Author: Augustine B. Makokha
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2152079284