Simulation model for planning mining operations

Варламова, С. А.; Volodina, Yu.I.; Zatonskiy, A.V.; Yazev, P.A.

doi:10.25018/0236-1493-2019-10-0-214-222

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Setting priority grades. One of the options of mining scenario management is allocation of available resources to different objects of a mine [20]. This is implemented by assigning outputs per extraction blocks and by setting priorities of some excavations over the other cuts.…”

Section: Mine Planning: a Case Study Of Cut-and-fill Methodsmentioning

confidence: 99%

Digital tools for underground mine planning: Cut-and-fill mining

Lukichev¹,

Nagovitsyn²,

Laptev³

2021

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Section: Mine Planning: a Case Study Of Cut-and-fill Methodsmentioning

confidence: 99%

Digital tools for underground mine planning: Cut-and-fill mining

Lukichev¹,

Nagovitsyn²,

Laptev³

2021

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“…Планирование добы-чи руды для каждого комбайна затруднено, так как помимо непосредственно отбойки руды им выполняется ряд дополнительных технологических операций, кроме того, определение движения самоходного вагона также затруднено. Ранее нами была создана имитационная модель [4], однако на момент создания она оснащалась только данными из открытых источников и нормативными данными. В реальности они могут отличаться не только от предприятия к предприятию, но и в пределах одного рудника и даже пласта.…”

Section: Consumption Dataunclassified

Mining Machine Simulation Model Refinement According to the Engines Energy Consumption Data

Zatonskiy

Yazev

2022

Applied Mathematics and Control Sciences

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The importance of mining factories planning for ore extraction is indicated. The created simulation model with open and normative data is presented. Significant deviations of the simulation results from the real data of the mining machines monthly operating time of are shown. The goal is to refine the model with the available mining machine operation real data. As a source of data, it is proposed to use miming machines engines energy consumption. The source this information is described. Information about the mining machine engines is given. That information can be used for mining machines analyzing to determine the times of the states. There are 8 such engines in total, their sequential switching on and off allows us to understand the time of 4 technological operations that make the greatest contribution to the mining machines operating time. A fragment of the algorithm which allows to determine the operations time is given. Two combines were selected with the production differs most from the model in the direction of increase and decrease. For both combines the duration of ore breaking and ore loading from the storage hopper to the self-propelled car is determined. First, an analysis was made for the mining machine that breaks more ore than was obtained in the calculations. The analysis showed the existence of a previously undescribed state of the mining machine-simultaneous ore breaking into the storage hopper and ore loading from the storage hopper to a self-propelled car After including that state in the model, a new calculation was made. Calculation shows increasing in the model total output. In addition, analysis was made of the existing stochastic delay during ore breaking. Analysis shows that statistical distribution stochastic delay can corresponds to a negative binomial distribution. Further, the study of data for a mining machine with the producing less than the model amount was carried out. It allows to study the stochastic delay during ore loading. It is shown that such a delay can also correspond to a negative binomial distribution. The simultaneous inclusion of described delays significantly reduces the total estimated production amount. In conclusion, it is discussed that the simultaneous addition of a new state and stochastic delays with a negative binomial distribution to the model significantly reduces the modeling error.

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“…При увеличении стохастических отклонений от заданных параметров результат имитационного моделирования значительно меняется [5,6]. В созданной нами системе [7] определяется большое количество обусловленных человеческим фактором стохастических параметров, статистические распределения которых неизвестны и не могут быть определены из наблюдений. Описанные имитационные модели горной добычи [8][9][10][11][12][13] указывают на наличие таких случайных факторов, но либо используемое в них распределение не указывается, либо используется только одно распределение для всех параметров (чаще всего нормальное).…”

Section: материалы и методыunclassified

Investigation of the Random Value Statistical Distributions Models Choice Influence on the Mining Operations Modeling Results

Zatonskiy¹,

Yazev²

2021

CTAC&R

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The importance of production planning for improving the performance indicators of a mining enterprise is indicated. The possibility of simulation modeling using for this aim is shown. It is shown that the created model has a large number of stochastic parameters. It is investigated that there is a problem of research lack about the choice influence of the mining modeling results with different statistical distributions. It is known that with an increase in stochastic deviations from the initial parameters, the productivity of queuing systems decreases. Purpose of work is to study this influence with four statistical distributions of a random quantity (uniform, normal, negative bi-nomial and Poisson distribution) for individual operations and their combinations. In addition, it is necessary to determine how much a change in one particular parameter will affect the overall result of the modeling. Materials and methods. In the previously created simulation model, a stochastic delay is added to the time of individual operations. The addition of such a delay with different sta-tistical distributions and with the same mathematical expectation is investigated. The simulation re-sults are compared with each other, for each individual operation the absolute and relative devia-tion of the results is shown. Further, a similar simulation is performed when all the simultaneously selected parameters changing. Result. It is shown that the magnitude of the deviation significantly differs among all deviations. It is shown that for various single changes in operations, the largest and smal-lest deviations can be given by different statistical distributions. To study the joint change with all parameters, 3 modeling scenarios are implemented: all uniform distributions (this case is used now), the scenario with the smallest deviation and the scenario with the largest deviation. It is shown that switching to another scenario leads to a significant change in the simulation. Conclusion. It is con-cluded that the used significant influence of statistical distributions choice to the accuracy of model-ing the operation of the mining machine is shown, especially when they are taken into account to-gether. The results can be used to clarify the influence of individual factors in the simulation model and improve the planning of potash mining operations, for individual mining machines too.

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Simulation model for planning mining operations

Cited by 5 publications

References 3 publications

Digital tools for underground mine planning: Cut-and-fill mining

Digital tools for underground mine planning: Cut-and-fill mining

Mining Machine Simulation Model Refinement According to the Engines Energy Consumption Data

Investigation of the Random Value Statistical Distributions Models Choice Influence on the Mining Operations Modeling Results

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