Room And Pillar Mining Method Pdf

By Tom F.
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21.05.2021 at 21:08
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room and pillar mining method pdf

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Note: This article focuses primarily on hard-rock room and pillar mining. Complimentary to this article, is a case study on post room and pillar mining which can be accessed here. Room and pillar is an underground mining method that has applications to a wide variety of hard-rock deposits worldwide.

But they are not advancing in automation as quickly as longwall equipment, with the exception that they are more often remotely controlled by an operator, rather than by an operator sitting on the machine.

types of mining pdf

Lutovac, Vesna M. Production planning in an underground mine plays a key activity in the mining company business. It is supported by the fact that mineral industry is unique and volatile environment. There are two uncertain parameters that cannot be managed by planners, metal price, and operating costs. Having ability to quantify and incorporate them in the process of planning can help companies to do their business in much easier way. Mineral deposit is represented as a set of mineable blocks and room and pillar mining method is selected as a way of mining.

Multicriteria clustering algorithm is used to create areas inside of mineral deposit that have technological characteristics required by the planners. We also developed a way to forecast the volatility of economic values of these areas through the planning period. Fuzzy linear programming model is used to define the sequence of mining of these areas by maximization of the expected value of the fuzzy future cash flow.

Model was tested on small hypothetical lead-zinc mineral deposit and results showed that our approach was able to solve such complex problem.

Certainly, the investment environment associated with the mining industry is unique when compared with environment encountered by typical manufacturing industries.

Some of characteristics which are often proclaimed as being unique are as follows. Virtually every knowledgeable observer would agree that mining ventures are extremely capital intensive. Even extremely small high grade precious metal mines employing only a handful of miners can rarely be developed for operation less than a million dollars. The amount of time required to develop a mining property for production can vary significantly.

Once the occurrence of an ore deposit has been well established, it takes a number of years of intensive effort before the property is brought on stream and ore is produced on a continuous basis. In addition to the obvious risks associated with capital intensity and long lead times, there are a number of other risks associated with mining ventures.

In general, these risks may be placed under the general headings of geological risks, engineering risks, economic risks, and political risks. Perhaps, the most unique aspect of the minerals industry is the fact that it deals with the extraction of a nonrenewable resource [ 1 ].

If we consider all these characteristics of the mining industry, we can conclude that production planning is essential activity which helps mining companies to do business in such environment. Planning is an optimization problem where the searching for the global optimum solution is very difficult and time consumption task.

There are many approaches that try to solve this optimization problem. Carlyle et al. Validation of this model was tested in one sector of the underground platinum mine [ 3 ]. Anani applied discrete event simulation to determine the optimal width of coal room and pillars panels under specific mining conditions.

She also tested the hypothesis that heuristic preprocessing can be used to increase the computational efficiency of branch and cut solutions to the binary integer linear programming problem of room and pillar mine sequencing.

The findings of her research include panel width optimization, a deterministic modelling framework that incorporates multiple mining risk in room and pillar production sequencing, and accounting for changing duty cycles in continuous miner-shuttle car matching [ 4 ]. Bakhtavar et al. Nehring et al. They represented all stope production phases by single binary variable and increased efficiency of mixed-integer programming in the process of optimization [ 6 ].

Bai et al. Optimization problem was treated as maximum flow over the adequate graph [ 7 ]. A general capacitated multicommodity network flow model has been used for long-term mine planning by Epstein et al. Grieco et al.

Terblanche and Bley reduced the resolution of underground mine scheduling problem and applied mixed-integer programming to improve profitability through selective mining [ 11 ]. Kuchta et al. Topal developed an early start and late start algorithm that defines the precedence restrictions for each mining unit in their mixed-integer linear programming model of the underground Kiruna Mine [ 13 ]. Hirschi developed a dynamic programming algorithm to supplant that trial and error practice of determining and evaluating room and pillar mining sequences.

Dynamic programming has been used in mining to optimize multistage processes where production parameters are stage-specific [ 14 ]. Gligoric et al. All developed mine production planning models were based on linear programming and different methods have been used to find the extreme value of the linear objective function, for example, simplex method, simulated annealing, Branch and Bound algorithm, ant colony optimization, neural networks, etc.

We applied fuzzy linear programming to incorporate the uncertainties in the objective function and make the problem of production planning more realistic. By this way, we increase the precision of the obtained results. If we take into consideration that mine production planning belongs to the decision making field then fuzzy model really helps us to make final decision in more efficient way.

The main aim of this paper is to provide efficiency support to decision making on production planning in underground mines that use room and pillar mining method as a way of mining. Model is based on the maximization of fuzzy objective function which represents the present or discounted value of the future cash flow of production plan, with respect to the set of constraints.

We consider this problem as zero-one linear programing problem in which only coefficients in the objective function are triangular fuzzy numbers. Coefficients represent the discounted economic value of the technological mining cut TMC which is a part of mineral deposit characterized with respect to the given set of technological requirements such as annual capacity of production, compactness of the shape of TMC, and standard deviation of ore grade in the TMC.

Total number of technological mining cuts is equal to the total number of years of production. The first step in the production planning model is related to the creation of TMC s having the value of attributes closely to the values of technological requirements. In the purpose of creation such TMC s clusters we developed fuzzy multicriteria clustering algorithm where uncertainties of some input data are quantified by triangular fuzzy numbers.

Mining engineers uses a block model of the deposit that represents the deposit as three-dimensional array of blocks. Accordingly, clustering algorithm is applied on the set of these blocks. The second step concerns the calculation of discounted economic value of TMC s.

It indicates that we are facing dynamic problem burdened with some uncertainties. These uncertainties come primary from the metal price and operating costs fluctuation through the time of planning. To estimate the future state of metal price, we developed forecasting algorithm which represents the hybrid of the fuzzy C-mean clustering algorithm and stochastic diffusion process called mean reverting process. This algorithm quantifies the future states of metal price by the fuzzy series.

Applying concurrently simulations of these two parameters we can estimate the expected fuzzy value of each TMC for every year of the planning time.

After that we discount these values by fuzzy discount rate and define the values of coefficient of objective function. Solution of the fuzzy objective function gives the order of mining of TMC s. The proposed model is a mathematical representation of mining business reality and allows mining company management to run a dynamic optimization of the business with uncertainty.

It helps mining company to survive in very risky environment. Production planning models, based on the linear programming, use block as a basic variable in the objective function. These models also use the constant values of metal price and operating costs through the planning time.

It means that these models are static from the point of view of these two parameters. If we want to include fluctuation of these parameters in the objective function, then number of variables significantly increases. Suppose we have a mineral deposit contains of 1 blocks, and we want to mine them for 10 years with a different metal price for every year, then the number of variables is about 10 Our model reduces the number of variables in the objective function by creation of TMC s.

It means that mentioned example would have only variables obtained as years of mining to the power of two. This reduction becomes more significant when dimensions of the block are small. By decreasing the number of variables, we enable uncertainties to be included in the model. We believe that including of uncertainties is much more important than maximum value of the objective function obtained by using blocks as variables with constant values of influencing parameters.

Applying fuzzy set theory and simulation of different stochastic processes we increased flexibility of the model and made the problem more realistic. The model was tested on a small hypothetical lead-zinc mineral deposit and results showed that model can be used for solving the problem of mine production planning. Fuzzy set theory, introduced by Zadeh, deals with problems in which a source of vagueness is involved and has been utilized for incorporating imprecise data into decision framework [ 16 , 17 ].

The characteristic function of a crisp set assigns a value either 0 or 1 to each member in X. This function can be generalized to a function such that value assigned to the element of the universal set X falls within a specified range, i. The assigned value indicates the membership grade of the element in the set A. The function is called the membership function and the set defined by for each is called a fuzzy set [ 18 , 19 ].

A fuzzy number is said to be a triangular fuzzy number if its membership function is given by For more details of arithmetic operations on triangular fuzzy numbers, see [ 16 , 18 ]. The absolute value of the triangular fuzzy number is denoted by and defined as follows [ 16 ]: A ranking function is a function , where F R is a set of fuzzy numbers defined on set of real numbers, which maps each fuzzy number into real line, where a natural order exists.

Let be a triangular fuzzy number then [ 18 ] Linear programming is one of the most frequently applied operations research techniques. In the conventional approach value of the parameters of linear programming models must be well defined and precise. However, in real world environment, this is not realistic assumption. In the real-life problems, there may exist uncertainty about the parameters.

In such a situation, the parameters of linear programming problems may be represented as fuzzy numbers [ 18 ]. In this paper, we consider zero-one linear programing problem in which only coefficients in the objective function are triangular fuzzy numbers. Such problem is first converted into an adequate crisp model and after that being solved by one of the existing methods.

Suppose we have a linear programming problem with fuzzy coefficients as follows: subject to Since variables x j and coefficients q ij are crisp values, it is necessary only to convert fuzzy objective function into crisp function. The process of conversion is based on the way developed by Kumar et al. Fuzzy objective function may be expressed as follows:. Example 1.

Fuzzy optimal value of our objective function is obtained by putting x 1 , x 2 , and x 3 in 8. The value of the given objective function is An important concept related to the applications of fuzzy numbers is defuzzification, which converts a fuzzy number into a crisp value.

Such a transformation is not unique because different methods are possible. The most commonly used defuzzification method is the centroid defuzzification method, which is also known as center of gravity or center of area defuzzification. The centroid defuzzification method can be expressed as follows Yager [ 20 ]: where is the defuzzified value. The defuzzification formula of triangular fuzzy number is This formula will be used in this paper.

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Lutovac, Vesna M. Production planning in an underground mine plays a key activity in the mining company business. It is supported by the fact that mineral industry is unique and volatile environment. There are two uncertain parameters that cannot be managed by planners, metal price, and operating costs. Having ability to quantify and incorporate them in the process of planning can help companies to do their business in much easier way.

Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. An underground mine consists of the portals entrance and exits to the mines , mains, submains, panels, and working faces. The panels are the working sections of the production operation. Depending on the mining method, a working section can have one or more working faces. The mining activities that traditionally take place to advance a working face further into the coal seam, called unit operations, include cutting, drilling, blasting, loading, and hauling the coal from the current position of the face to the planned advance distance position of the face.

The most common mining system is room-and-pillar. In this system a series of parallel drifts are driven, with connections made between these drifts at regular intervals. When the distance between connecting drifts is the same as that between the parallel drifts, then a…. The oldest of the basic underground methods, room-and-pillar mining grew naturally out of the need to recover more coal as mining operations became deeper and more expensive. During the late s, conventional techniques began to be replaced by single machines, known as continuous…. A common open-stoping method is room-and-pillar mining, in which pillars of ore are left standing to support the rock over a flat-lying ore body.

The method used is the room and pillar mining method where the initial entry galleries are driven into the coal seam starting from the surface.

Mining methods and method selection

Room and pillar variant of breast stoping , is a mining system in which the mined material is extracted across a horizontal plane, creating horizontal arrays of rooms and pillars. To do this, "rooms" of ore are dug out while "pillars" of untouched material are left to support the roof overburden. Calculating the size, shape, and position of pillars is a complicated procedure, and is an area of active research.

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Ebrahimabadi, Arash, and M. One of the major issues in room and pillar mining is to design of optimum pillar dimensions. The aim of this research is to design the optimum pillar dimensions for layer C1 in Parvadeh coal mine, resulting in significant improvement on pillar recovery and productivity for the mine.

Mining methods and method selection

Address : No. Application and general administration Regulation 1Application 1 These Regulations apply to a the conveyance, storage, possession, manufacture and use of explosives for mining, quarrying and civil works and.

Hard-rock room and pillar

 Т-ты… - заикаясь, он перевел взгляд на ее непроколотые уши, - ты, случайно, серег не носила. В ее глазах мелькнуло подозрение. Она достала из кармана какой-то маленький предмет и протянула .

Слишком рано. Слишком рано. Беккер беззвучно выругался. Уже два часа утра. - Pi'dame uno. Вызовите мне машину. Мужчина достал мобильник, сказал несколько слов и выключил телефон.

Post room-and-pillar mining (Figure ) applies to inclined ore bodies with dip angles from 20° to 55°. These mines have large vertical heights where the mined​-.

Может быть, он что-нибудь поджег. Она посмотрела на вентиляционный люк и принюхалась. Но запах шел не оттуда, его источник находился где-то поблизости. Сьюзан посмотрела на решетчатую дверь, ведущую в кухню, и в тот же миг поняла, что означает этот запах. Запах одеколона и пота.

 - Мой человек ликвидировал его, но не получил ключ. За секунду до смерти Танкадо успел отдать его какому-то туристу.


Ray B.
24.05.2021 at 00:30 - Reply

PDF | Practical importance of the Room and pillars method Different applications of the R & P method R & P in hard rocks: Conditions of deposit.

Corrado R.
30.05.2021 at 07:25 - Reply

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