Feed Distribution Probabilistic Estimation in Mineral Processing Units Using Markov Chains

In the conventional mass balance methods, sampling are carried out to determine the metal content, solid percentage and particle size distribution in the paths (branches) and ultimately the corresponding errors are dispersed. In these methods, there is no information about the amount of feed and the grade (or the metal content) at any moment of time for each mineral processing unit (plant equipment). Thus, in this paper, the quantitative and qualitative values of input feed to each of the operating units in the processing paths were probabilistically investigated using stochastic process theory, and consequently the load distribution in these units was estimated. For this purpose, the processing plant was firstly modeled as a directional and balanced graph. In this graph, each node represents an operating unit and each edge displays a path between two units. The weight of each edge also indicates the passing material weight from the node to another node, which is assigned as a random variable at a specified interval. After modeling the process, the obtained graphical model containing random parameters was analyzed using Markov chain theory. Applying the philosophy outlined in this paper, not only the load distribution, but also the presence probability of each particle or mineral in each of the operational units of the processing plant can be predicted. It is also possible to estimate the tonnage in each unit when the plant is shut down and even the necessary steps are provided to discharge them. In this research, Chadormalu iron ore processing line was modeled using method presented and the results were analyzed and thereafter were reported using probabilistic parameters.