Overflow ball mills have found popular application in the ore dressing process for post-primaryudgrinding firstly owing to their ability to produce finer grinds, necessary for efficient mineraludliberation and better flotation recovery and secondly due to lower initial capital outlay. Howeverudthey are inefficient and intensive energy consumers. This trend has been exacerbated in the wakeudof increased installation of large diameter ball mills to benefit from economies of scale, coupledudwith diminishing ore quality currently being experienced by mines worldwide. To fully utiliseudthe available mill capacity and achieve optimal performance whilst maintaining energyudefficiency for these large devices, closer and more effective control is needed. Satisfaction of thisudneed would result in stability of the entire mineral processing circuit, thereby reducing theudoverall cost in mineral extraction. Clear and deeper understanding of the in-mill behaviour isudfundamental to the realisation of the above objective.udThis thesis explores several experimental and modelling techniques to obtain deeperudunderstanding of the internal behaviour of an overflow ball mill. A direct load sensor comprisingudan inductive proximity probe and a conductivity probe installed through the mill shell has beenudutilised to collect information of the media and slurry dynamic positions inside a laboratory balludmill while a commercial on-line ball and pulp sensor was employed to collect similarudinformation on an industrial overflow ball mill. Useful insights were acquired that can help theuddesign of control strategies for optimal mill performance. Four feature variables, i.e. dynamicudmedia angle, slurry pool angle, conductivity signal amplitude and the slurry pool depth, derivedudfrom the sensor signals data were characteristically influenced by changes in mill operationaludconditions. Therefore the possibility of using these features to predict the associated milludoperational variables is feasible. In view of the findings, two multivariate models, one based onudthe concept of data projection to latent space (PLS) and the other combining PLS and radial basisudfunctions neural networks (RBF) were built and applied to predict the in-mill slurry density andudball load volume. Both models yielded adequate predictions, albeit the hybrid PLS-RBF modeluddisplayed marginally better prediction performance. The results are indicative of the availableudpotential for mill on-line monitoring and control by multivariate techniques based on relevantudfeatures contained in the media and slurry sensor signals data.udIn another endeavour, a gamma camera was successfully employed to study the flow and mixingudbehaviour of slurry inside a laboratory mill using Technetium-Tc99m radiotracer as a flowudfollower. The effects of slurry viscosity and mill rotational speed on slurry mixing rate within theudball charge and slurry exchange rate between the pool and the ball charge were assessed, yieldingudinsightful data. However, the results remain inconclusive as only qualitative information couldudbe 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 acquiredudon an industrial mill through salt tracer tests was adequately analysed to assess the variation ofudslurry residence time distribution (RTD) and volumetric holdup inside the mill as affected byudchanges in slurry concentration and ball load volume. A model based on the concept of serialudstirred mixers with a plug flow component produced fairly accurate predictions of the RTD data.udAlso, equations derived from a mathematical description of the dynamic load profile producedudgood estimates of the in-mill slurry volumetric holdup.udFurther, an improved mixing-cell model was developed and applied to characterise the in-milludslurry hydrodynamic transport based on the measured RTD data. The model was able to accountudfor the effects of non-ideal flow conditions such as slurry back-mixing, slurry exchange betweenudthe pool and ball charge and bypass flows on the main flow of slurry thus giving correctuddescription of the inherent in-mill slurry transport dynamics. Note that failure to tune the milludappropriately to achieve desirable in-mill slurry transport behaviour may result in poor millingudperformance and corresponding high energy expenditure.udThus, the results obtained in this thesis clearly demonstrate that, a combination of experimentaludtechniques and mathematical models is a viable route to enhance understanding of mill internaludbehaviour, which in turn enables development of better control schemes for optimal milludperformance.
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