Mathematics and computer science competence has become crucial in order to satisfy constant mining technical challenges faced by buy mining hardware engineers. These challenges may exist in the areas of actual mineral extraction or the managing of labor costs or other domain of the industry.

Mineral activity in years past did not heavily include the use of computers in the mining process. Mining was different in some ways where, say, practical knowledge on the use of percussion drilling and explosives in tunneling was critical. The introduction of tunneling big-rig hardware and other equally high performance mining equipment, changed the landscape slightly in which mastering knowledge in fields of rock mechanics, applied mathematics and fluid mechanics became more critical.

The mineral industry has routinely increased the use of sophisticated numerical algorithms to derive suitable production day-to-day schedules in complex mineral operations, where the direction is pointed at utilizing larger and more complicated mathematical models. There is also a heightened industry focus on numerical models and other efficient methods for numerical treatment of control problems whenever these arise in the decision-making process.

In this framework, optimizing mineral extraction and decision-making are challenging and interesting. Fortunately, there are many mathematical algorithm models to help along the way. For example, if dealing with statistics analysis based on experimental observations, a variety of techniques like Fisher-Snedecor or Least-Square Fit distributions and other regression methods are widely applied. When confronted with specific rock mechanics intricacies methods following Runge-Kutta theory may provide adequate solutions. As a general rule, specialty areas of function evaluation, interpolation, iterative algorithms, series, linear algebra, statistical analysis, optimization, linear and nonlinear systems are heavily used in mining applications.

For mining to develop and apply such complicated, multidimensional models necessitates, however, a well-trained and experienced staff with expert knowledge of numerical analysis techniques, computational procedures and mining itself. Today, strong indications substantiate that people of this profile are not too frequent in the mining industry.

It is quite evident that complex computer mathematical algorithms do indeed provide the tools for methodological progress and are most welcome and helpful in improving the mining engineer’s problem-solving capacity. They are the right tools and equally important is the task of equipping computing know-how into the mining engineer’s toolbox.

The tremendous steps in computer technology brought new dimensions to mineral training. University training programs could be modified to accommodate demand of computer science oriented mining engineers with specialty fields of practice to satisfy industry needs.

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