فصلنامه مهندسی معدن
پیاپی 2 (پاییز 1385)

The Lerchs and Grossmann algorithm is a rigorous technique Based on Dynamic Programming, developed for optimization of open pit limits. This algorithm guarantees the true optimum solutions in 2D sections, but is may be used only for pit slope constraints of 1:1, 2:1 or more. In circumstances, where the geomechanical conditions impose less slopes such as 1:2, an adjustment of the block sizes and reconstruction of the block model is needed. A new algorithm is introduced in this paper to overcome this shortcoming. The conventional block model is transformed into an intermediate and a final block model to reflect the pit slope constraints. Then the modified Lerchs and Grossmann algorithm consisting of a recursive formula with two criteria is implemented on the final model. Using this algorithm, there is no need for reconstruction of the block model.

Evaluation of gravity separation of minerals usually is accomplished with two indices. The first is liberation degree and the second is separation accuracy or Escort Probable (Ep). This research is a case study on ALBORZ_E_SHARGI coal washing plant wastes for the evaluation of two essential indices. It was found that the indices representing a numerical value cant give sufficient information required for decision. Thus efforts took place to improve previous indices and to represent a method for accurate characterization of feed. It was obtained a suitable product for steel industry in two stages. Gravity concentration step by cut point of 1.75 t/m3 is necessary to produce primary concentrate followed by grinding it to -2mm and rewashing to obtain final concentrate. In the first step ash content reduced from 66% to 31.3% and the reduction of ash content to 14% in final washing was possible.

The adsorption of lanthanum (III) and cerium (III) with some of metal ions has been studied. Cyanex 302 (bis[2,4,4-trimethyl pentyl] monothio phosphinic acid) as the stationary phase and polymeric styrene divinylbenzene resin, AmberliteXAD-4 as the support material were used in chromatographic studies of lanthanides and some metal ions. The content of cyanex 302 in the resin was determined 40.88%. The influence of various parameters, such as time, acid concentration, temperature and pH of aqueous phase was evaluated. Also uptake profiles of various ions, such as Al(III), Fe(III) and Mn(II) were obtained as a function of acidity by batch studies using Cyanex 302/XAD-4. The results showed that pH of aqueous phase had the most effect on uptake of lanthanides and metal ions. The resin showed the good uptake for lanthanides extraction. Uptake value for lanthanum and cerium was determined 97% and 70% respectively.

This paper presents the results of first stage investigation of phosphate recovery from fine particles in Esfordi phosphate beneficiation plant. The aim of this investigation was the optimization of flotation process for increasing the phosphate recovery and decreasing the Iron content of concentrate. Experimental design, Taguchi method, was applied to investigate the effects of 8 factors namely, collectors ratio, collectors dosage, pH, starch dosage, pulp density, conditioning time for starch, conditioning time for collectors and frothing time. 4 responses, namely phosphate recovery, phosphate grade, iron recovery and iron grade were measured and evaluated to determine the optimum conditioning of each response. The orthogonal array L27(313) was used for the evaluation of the above factors on the responses of design. Statistical analysis of results showed that starch dosage, pulp density an frothing time had more impact on the phosphate recovery. The optimum condition for each response was introduced and the best condition for phosphate grade in concentrate was compared with the ruling condition in beneficiation plant. The comparison of results showed acceptable improvement in concentrate quality.

Permeability, or flow capacity, is the ability of porous media to transmit fluid. To understand reservoir performance and in case of reservoir management and development requires accurate knowledge of permeability. The permeability of the formation is usually determined from the cores and/or well tests. It should be noted that cores and well test data are often only available from few wells in a reservoir while the logs are available from the majority of the wells. Therefore, the evaluation of permeability from well log data represents a significant technical as well as economic advantage. Many fundamental problems remain unsolved by most predictive models. This paper introduces the use of an improved neural network trained by a Back Propagation learning algorithm to provide solution for the permeability prediction from well log data. An Iranian offshore gas field is located in the Persian Gulf, has been selected as the study area in this paper. Well log data are available on substantial number of wells. Core samples are also available from a few wells. It was shown that the neural network system is the most effective method in predicting permeability from well logs.

The proper characterization of reservoir heterogeneities plays an important role in reservoir evaluation and forcasting of its performance. However, the heterogeneity of a reservoir and its property distributions in the large scale of reservoir volume could not be properly determined and estimated with known data from a few wells. In such a case, one of the newest and roboust methods in reservoir characterization is the use of fractal methods whenever the geostatistics fails. The objective of this paper is to introduce and discuss a new method that is the combination of fractal geometry (small scale) and geostatistics (large scale) to characterize the reservoir property distributions in a gian reservoir with a few known well data. The developed method was applied to a real reservoir in the south west of Iran with three exploration well data. In this method, first of all, the reservoir zonation and formation units including carbonate, sandstone and shale must be identified from the petrophysical data by using multivariate statistics analysis. So that, the porosity, permeability, and water saturation for the known wells could be estimated from the core and log data. Then, spatial correlation using variogram analysis and quantify heterogeneities using R/S analysis, can be determined for each zone in the wells. Eventually, merging kriging method and fGn noise of fractal will lead to develope our new method to estimates and predict the reservoir property distributions between wells and throughout the reservoir more accurate and very precise than the previouse methods. The produced error is in acceptable and accurate range that was less than 4.93 percent for porosity for instance. The output of the model may be used as an input for geomodeling and simulation softwares. Also, heterogeneity distributions of reservoir characteristics are well visualized by fractal distributions.

There are few algorithms, developed for optimisation of ultimate stope limits. These are either heuristic or rigorous. The rigorous algorithms such as the application of the branch and bound technique and dynamic programming approach do not apply on 3D problems. Heuristic algorithms such as floating stope of Datamine and the Maximum Value Neighbourhood (MVN) algorithm do not guarantee the true optimum solution; they only provide a solution, which is close to the optimum one. The MVN algorithm runs on a fixed 3D economic block model of the ore-body and forms the best neighbourhood of each block in the model. The algorithm starts from the first block to form its MVN and include it into the ultimate limit. Then it proceeds to the last block to build up the small MVN as largely as possible. However, since it is a heuristic approach, the final stope limit obtained may contain a number of unnecessary waste blocks, ie they may be excluded from the ultimate stope without violating the stope constraints. In addition, there may be a number of necessary ore blocks, excluded from the ultimate stope. This paper introduces the extension of the MVN algorithm to run a multiple pass and check if it is possible to remove waste blocks from the ultimate stope and add other ore blocks to it. Running the multiple pass makes the ultimate stope limits as close as possible to the optimum one.