Multiple criteria decision making (MCDM) is a supporting tool which is widely spread in different areas of science and industry. Many researchers have confirmed that MCDM methods can be useful for selecting the best solution in many different problems. In this paper, two novel methods are presented and applied on existing decision-making processes in the mining industry. The first method is multiple criteria ranking by alternative trace (MCRAT) and the second is ranking alternatives by perimeter similarity (RAPS). These two novel methods are demonstrated in decision-making problems and compared with the ranking of the same alternatives by other MCDM methods. The mining process often includes drilling and blasting operations as the most common activities for exploitation of raw materials. For optimal blasting design it is important to select the most suitable parameters for the blasting pattern and respect characteristics of the working environment and production conditions. By applying novel methods, how to successfully select the most proper blasting pattern respecting all conditions that must be satisfied for economic aspects and the safety of employees and the environment is presented.

Accurate metal price forecasting is the precondition for optimal and sustainable mine production planning. This paper combined two methods for time series analysis. The developed model represents the combination of the Grey System Theory and a Stochastic differential equation. More precisely, we added stochastic term to the first-order whitenization differential equation. Solution of this equation represents the time response function which is capable of creating artificial evolving paths of the metal price. The simulation process resulted in a distribution and adequate expected value at every single point. Further, model efficiency was increased by adding residuals modeled by the Singular Spectrum Analysis method. The model was tested on the monthly lead metal price series. Mean absolute percentage error is 4.37% and the model can be classified as a high-performance model.

Multi-criteria decision making (MCDM) refers to methods and processes used to evaluate and prioritize options when multiple criteria are involved. Extensive research conducted in the past has shown that uncertainty is a pervasive challenge in the decision-making process, particularly in complex and dynamic environments, such as mining. When making decisions under uncertainty, especially in stope and fan pattern selection for underground mining, it is crucial to consider various aspects, including economic and production factors. This paper presents an application of simulation combined with the ranking alternatives by perimeter similarity (RAPS) method for optimal stope and fan pattern selection in an underground mine. The results obtained were compared with existing MCDM methodology technique for order of preference by similarity to ideal solution (TOPSIS) and showed a very high correlation, which indicates the applicability of new methodology for decision making in mining industry problems.

Mining is a global industry that is of great importance for every product which is used by human. For mining, process efficiency, reducing production downtime, increasing profitability are all very important. Soft computing technologies (SC) are helping in the process of transforming the mining industry into a safer and more environmental friendly industry, but keeping in mind the financial aspect as well. In this paper some of fields of blasting activities in which the SC methods have been applicated, will be presented. Trough the following chapters some of the most significant researches will be reviewed.

Underground mine ventilation serves both as a critical safety system and as one of the major consumers of electrical energy in underground mining operations. This paper presents a critical review of the energy performance of ventilation management strategies, focusing on ventilation network optimization, fan speed control, Ventilation-on-Demand (VoD), model-based control, computational fluid dynamics (CFD) applications, and sensor-based monitoring. The review is organized according to the level of intervention and validation, encompassing industrial measurements, pilot-scale and laboratory studies, as well as numerical simulations. The findings indicate that direct energy savings are most commonly achieved through the reduction of network resistance and pressure losses, fan speed adjustment, and the spatial and temporal alignment of airflow distribution with actual operational requirements. CFD analyses and monitoring systems do not constitute energy-saving measures by themselves; rather, they provide the basis for defining safe operating limits and enabling closed-loop control strategies. Reported energy savings are not directly comparable due to differences in baseline operating conditions, system boundaries, and validation methodologies. Reliable implementation requires the maintenance of minimum safety airflow rates, the provision of backup safety measures, and automatic transition to predefined safe operating modes in the event of sensor, communication, or actuator failures.

This paper gives a brief overview of the sepiolite deposits, the basic properties, and the possibilities of using this mineral. Particular attention is paid to sepiolites from domestic production, especially the use of sepiolites suitable for the development of small businesses and technologies, with a focus on environmental protection. Aim of this paper is to point out importance of this mineral and to draw attention to the use of domestic raw materials. With appropriate mechanical and chemical pretreatments, sepiolite is shown to be useful in following applications: NCR paper, drilling fluids, absorbents, carriers for chemicals, animal nutrition, agriculture, decolorising agents, cosmetics and pharmaceutical uses, etc.