Conference Proceedings
Twelfth International Symposium on Mine Planning and Equipment Selection (MPES 2003)
Conference Proceedings
Twelfth International Symposium on Mine Planning and Equipment Selection (MPES 2003)
Mineral Processing Plant Simulator in Continuous Surface Mining
Mineral processing is an essential component of a mine production chain and it accounts for significant proportion of the production cost. Over the past decade, significant research progress has been made in processing plant efficiency using theoretical, experimental and simulation modelling techniques. However, these studies are limited to process plant system as stand-alone units with no interactions with the mining units. In order to maximise the total mining system efficiency and more accurately simulate the system, the processing plant must be modelled as part of an integrated mining system. This becomes increasingly important in continuous surface mining systems, with limited ore processing capacity. In addition, plant efficiency is roughly estimated with known parameters or with simplistic statistical data, without regards to the stochastic processes governing these parameters, such as processing plant feed introduction. In this paper, a comprehensive model of the ore processing plant system is given and the object-oriented design (OOD) of the system is briefly introduced. The joint operation of ore processing plant system and haulage system is analysed. Numerical modelling is used to analyse the ore processing plant system under different conditions. Analysis of the results show that the optimum plant capacity is 0.7 tons/sec (2520 tons/hour). The results also show that the plant system resources cannot be fully utilised because the production rate is always less than the plant capacity. When plant capacity reaches an optimum value 0.7 tons/sec, the production rate is about 0.64 tons/sec. The underlying stochastic processes associated with plant and mine capacities cause this variability. In order to reduce this variability, there is the need to tightly set tolerances for the function variables.
Contributor(s):
S Frimpong, G-K Er, R S Suglo
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- Published: 2003
- PDF Size: 0.439 Mb.
- Unique ID: P200301034