Combining Indicators: Maximizing Mining Returns In Manchester City

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Combining Indicators: Maximizing Mining Returns In Manchester City

Combining Indicators: Maximizing Mining Returns In Manchester City

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Mining extraction planning has a broad impact on production management and the overall economic efficiency of mining companies. The traditional method of preparing underground mine production planning is complicated and tedious, and it is difficult to achieve optimal calculation results. First, multi-objective optimization theory and methods are used to establish a multi-objective planning model with the objectives of economic efficiency, grade and best ore quantity, taking into account the fluctuation limits of ore grade, ore output of ore. mining, mining business production capacity, and utilization of mineral resources. Second, the improved particle swarm algorithm is applied to solve the model, a nonlinear dynamic weight loss strategy is proposed for the inertial weights, the variation probability of each generation of particles is dynamically adjusted to the degree of aggregation, and this variation probability is used to perform mixed Gaussian and Cauchy mutation for the global optimal position and adaptive wavelet variation for the worst individual optimal position. This improved strategy can increase population diversity, improve the algorithm convergence speed globally, and avoid premature solution convergence. Finally, taking the example of a large polymetallic underground mine in China, the calculation example proves that the yield of the algorithm solution is 10.98% higher than the mine plan index in terms of ore volume and 41.88% higher in terms of economic efficiency, the speed algorithm solution is 29 .25% higher, and its optimization model and algorithm meet the requirements of the mining industry extraction production plan, which can effectively optimize mine extraction plans and provide the basis for mine operation decisions.

The preparation of an extraction plan, as the basic link in the production operation of a mining company, is one of the most important tasks in a mine’s production decisions, and the rationality of preparing a plan directly affects the efficiency of subsequent production. relationships and overall economic efficiency of mining companies [1, 2, 3]. Traditional manual preparation methods are not only time-consuming and intensive but also have poor accuracy and are difficult to modify. The main reason is the complex conditions of underground mines during plan preparation, which require comprehensive consideration of the spatial and temporal constraints between production and mine processes and their sustainability. Therefore, how to quickly and accurately prepare an underground mine production plan is an urgent problem to be resolved.

Combining Indicators: Maximizing Mining Returns In Manchester City

With the continuous advances in computer technology and operations research theory, many researchers have begun to try to use the powerful computational power of computer simulation to simulate mine production processes, so as to continuously optimize mine extraction plans [4, 5]. Some researchers admit that integer programming can solve the problem of discrete production scheduling decisions in the mining industry [6, 7]. Many studies on mining production planning related to integer programming theory were then carried out [3, 8, 9, 10, 11, 12]. Dimitrakopoulos and Ramazan [13] developed an optimization framework for stochastic mine production scheduling taking into account mine uncertainties based on the ore body model and an integer planning approach. Weintraub et al. [14] developed a large aggregate integer planning model (MIP) based on cluster analysis for mine planning at CODELCO, a national copper mine in Chile, through which CODELCO mine-wide data information can be obtained to optimize mine extraction planning. Newman et al. [15] developed a mixed integer planning model for underground mining operations at the Kiruna mine, Sweden. This optimization model identifies operationally feasible recovery sequences that minimize deviations from planned production quantities. Terblanche and Bley [16] used a mixed integer planning approach to construct a theoretical model that could be applied to the optimization of extraction production plans for open-pit and underground mines but did not verify the feasibility of the model. Nehring et al. [17] proposed a better modified model formulation for the classic long-term mine planning model, by assigning different human resources and equipment to each mining area. In the classic model, only one binary variable is assigned for each activity in each mining area, while the improved model assigns one binary variable for all activities under stricter assumptions.

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Although there is more research on mathematical planning methods, most of these models achieve a mine production planning solution with a single economic indicator; however, since mine extraction production planning is a complex engineering system, it is difficult to highlight production plan preparation and optimization effects by considering only one economic indicator [18]. To overcome this difficulty, researchers have used various computational intelligence methods to solve multi-objective prediction and optimization problems, and heuristic algorithms are an effective method for increasing solution speed and avoiding the involvement of local optimal solutions, while having unique advantages for multi-objectives. -objective optimization problem (MOP). Little and Topal [19] investigated a whole life of mine (LOM) production planning methodology generated using simulated annealing techniques and a stochastic simulated representation of the ore body with the aim of maximizing the net present value (NPV) of the mine. Hou et al. [20] discussed mine production planning for the next three years using an artificial bee colony optimization algorithm. Otto and Bonnaire [21] developed the program “Greedy random adaptive search” to help solve copper mining development models and increase the solution speed. O’Sullivan and Newman [22] proposed a heuristic optimization algorithm to establish a complex set of constraints based on an optimization algorithm in a mining operations model for an underground lead-zinc mine in Ireland. Wang et al. [23] proposed the formulation of a multi-objective optimization model by taking the quality of the mined and processed ore as the main constraint, maximizing mining profits and natural resource use efficiency as the objective function, and using a genetic algorithm to find the optimal solution to the problem. multi-objective optimization problems. Nesbitt et al. [24] considered the uncertainties in the economic value of minerals faced by mines with long operating cycles. For hard rock mines, stochastic integer programming methods help mines adjust mining schedules with a high degree of feasibility.

However, the use of traditional heuristic algorithms in the preparation of mining industry extraction production plans leads to slow solution speed and reduced global convergence performance, which brings many difficulties to the preparation and optimization effect of actual production operation plans in real-time and has an adverse impact. great impact on the production management and economic efficiency of enterprises. Therefore, to overcome these problems, which are caused by the complexity of multi-metal mine production planning, and the difficulty of achieving optimal results with traditional methods, this paper takes the best economic efficiency, grade and volume of ore. as an objective and integrates constraints such as fluctuations in ore grade, ore output from the mining site, mine production capacity, and mineral resource utilization. A production planning model is created, and the model is solved optimally with an improved particle swarm optimization (PSO) algorithm with nonlinear inertial weighting and adaptive mutation probability (NAMPSO) in the context of an engineering example to improve the model solving speed and improve the model solving speed. global convergence performance of the algorithm, thereby verifying the feasibility of the model solution method.

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