The mining production systems, both for underground and open pit extraction consist mainly in a string of equipment starting with the winning equipment (shearer loader, in case of underground longwall mining or bucket wheel excavator in case of open pit mining), hauling equipment (armored face conveyor in longwall mining or the on-board belt conveyor in case of excavators), main conveying equipment (belt conveyor in both cases), transfer devices, stock pile or bunker feeding equipment. This system of mainly serially connected elements is characterized by the throughput (overall amount of bulk coal respectively overburden rock produced), which is dependent on the functioning state of each involved equipment, and is strongly affected also by the process inherent variability due to the randomness of the involved processes (e.g. the cutting properties of the rock). In order to model and simulate such production systems, some probabilistic methods are applied arising from the artificial intelligence approach, involving unit operations and equipment, as the overall system as a whole, namely the Monte Carlo simulation, neural networks, fuzzy systems, and the Load Strength Interference methods. The results obtained are convergent and offer the opportunity for further developments of their application in the study of mining production systems
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