Ore resource modelling is an essential aspect of mining operation. It is also a crucial pre-mining step required for a successful exploitation of mineral deposits. Ajabanoko iron ore resource model was developed and the ore reserve estimate carried out using inverse distance method as contained in Surpac 6.4.1 mine software. The total number of blocks used for the model is 54,475. Ore estimation result obtained from thirteen drill hole data indicates 38,313,595 tonnes of iron ore and density of 3.65 tonnes/m3. The average grade and total volume of the ore body is 36.36 % and 10,496,595 m3 respectively.
Every mineral processing plant flowsheet is selected from several possible alternatives because an ore may be processed using a number of recovery techniques. The flowsheet that is finally selected must have certain technical or economic advantages over the others. In a redesign process of the Nigerian Iron Ore mining Company (NIOMCO) processing plant, Itakpe, five design options (described here as New Design Options A, B and C and Improved Existing Plant Options A and B) were developed as alternatives to the existing plant. Concentrates' properties, recovery ratio or efficiency and efficiency ratio are the technical parameters used as measure of plant performance. The properties analyzed for comparism include iron mineral content of the concentrate, total concentrate weight, concentrate grade, percent recovery and loss. The analyses were done by stepwise iteration of all streams in the flowsheet from comminution to the final concentrate. The results of the analyses show that the concentrate that has the best value for the new iron ore plant options is that produced by the new plant design option C (which employs floatation as the only recovery process) which has a concentrate grade of 70% iron mineral content representing a recovery of 95% and a loss of 5% respectively. This gives the best overall performance.
The research work examines the effects of controllable blasting variables on number of boulders generated after blasting. The objective of the research was achieved through collection of data related to blasting which are drill hole depth, drill hole diameter, burden, spacing, average charge per hole, and specific charge. The collected data were analysed statistically using both Microsoft Excel Software and SPSS Software. The result of the analysis reveals that all the input controllable blasting variables which are drill hole diameter (X1), drill hole depth (X2), hole spacing (X3), burden (X4), average charge per hole (X5), specific charge(X6) that participated as independent variables in the models are found to be significant and the R2 values obtained from the graph show a very strong correlation between the number of boulders generated after blasting and the input variables except that of drill hole diameter which shows a very weak correlation. The equation generated using the SPSS could be used to determine number of boulders generated after blasting.
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