grade

In the present research, a probabilistic neural network based on the Bayesian probabilistic ‎algorithm was employed to classify the grade of Ali-Abad copper deposit in Yazd. For this ‎purpose, induced polarization (IP) and resistivity (Rs) geophysical data and rock type of ‎exploration borehole cores as geological information corresponding to four geophysical ‎profiles, DD-1, PD-2, PD-3 and PD-4 were used as input parameters as well as the copper ‎grade of the boreholes as target parameter. To achieve the goal, 488, 528, 188, and 456 data ‎were randomly collected from the sections related to DD-1, PD-2, PD-3 and PD-4 geophysical ‎profiles so that 75% of total data were selected for training and 25% to test the probabilistic ‎neural network. The performance of the proposed approach was evaluated by confusion ‎matrix through the ratio of summation of data on the main diameter to the total test data, as ‎well as determination of Commission and Omission errors. The results of the research show ‎that the probabilistic neural network could estimate the test data for DD-1, PD-2, PD-3 and ‎PD-4 profiles with accuracy of 60, 74, 60 and 83.3%, respectively which are reasonable ‎considering the type of available data. In addition, the results were qualitatively evaluated ‎through plotting isograde maps of four exploratory cross-sections over the geophysical ‎profiles. This process was carried out using the assay data of exploration boreholes, gridding ‎and the grid interpolation with the high accurate kriging estimation method, which was leaded ‎to favorite results.‎

The evaluation of a magnetic separation circuit can be used to determine its working efficiency, and formulate new strategies for its future performance improvement. The aim of this research is to do performance evaluation for units number 2 and 6 of the magnetic separation circuits in Sangan iron ore complex. In both units, the run of mine ore is crushed and divided into coarse (10-30 mm) and fine (0-10 mm) sized fractions, and then each fraction proceeds to a dry low intensity magnetic separation circuit. Totally, 270 samples were taken from four magnetic separation circuits and the samples were analyzed for total iron, iron oxide (FeO) and sulfur content. The results indicated that the average Fe grade of the final product of all magnetic separation circuits was more than 57%. The average iron recoveries in the fine and coarse magnetic separation circuits for unit number 6 were 92.29 and 78.93%, and for unit number 2 were 69.25 and 55.36%, respectively. The iron recovery of coarse magnetic separation circuits is lower than that of fine circuits which is due to a lower magnetic field intensity of separators in these circuits. This is for maintaining the suitable iron grade of concentrates desspite the lower degree of freedom in the feed. The iron recovery in both magnetic circuits of units number 2 is lower than that of units number 6 which is due to inappropriate feed and operating conditions of the magnetic separators in this unit.

The variation in tonnage and average grade vs. cut-off grade، called grade-tonnage curves، is one of the most important factors in investment، risk assessment and uncertainty for exploitation of a deposit. These curves are plotted with the use of exploration data and through classic and geo-statistical methods. In this paper، new fractal methods have been used to plot these curves. In fractal methods، without replacing or omitting outliers، one can fit a power-law function to data. Then، using this relation، one could calculate volume (using thickness variable)، average grade (using grade variable) and metal content (using grade-thickness variable) and then with these parameters، grade-tonnage curves are plotted. In this case study one of the mineralization zones of Koh-e-Zar gold deposit is used and through using number-size and concentration-area fractal models tonnage-grade curves have been plotted. Finally، the results are compared with traditional geo-statistical methods that indicate the minimum difference between geo-statistics and fractal methods in resource estimation is zero and the maximum difference is 22. 2%. These differences for average grade estimation are 0. 3% 14. 4% respectively. The descriptive statistics proves no significant difference between the results. The most important advantages of applying fractal method include the use of initial data، the possibility of estimation with irregular and low-density data، and easier calculations.

Grade estimation is one of the important stages for mining assessment. Grade values have asignificant effect on scheduling, designing and management of the mine. Therefore, it is importantto apply a method, which is able to estimate the necessary parameters with a high accuracy. One ofthe direct methods for figuring out the grades is to use exploration wells, which, because of theirhigh costs, usually it is impossible to use them extensively. In this study, a novel method based onfuzzy logic, neural networks and genetic algorithm is presented. This algorithm, by applying geneticalgorithms for optimization of the architecture and the neuro-fuzzy parameters, is able to achievebetter results as compare with other traditional methods for grade estimation. This is because ofusing different artificial intelligent methods. Foe this aim, Darrehzar copper porphyry was selectedas the case study. According to the results obtained our proposed algorithm able to capture theexisting pattern between the inputs and outputs finely and to estimate the grade with a highprecision.