Open Pit Optimization Using Artificial Neural Networks on Conditionally Simulated Blocks

Design and optimization of open pit limits are of paramount importance because they provide information for evaluating the economic potential of a mineral deposit and for developing short and long range mine plans. Many algorithms and their modifications have been used to design and optimize open pit limits. These algorithms have provided mine planning engineers with pertinent information in designing, optimizing and extracting ore reserves by open pit technology. However, they do not address the random field properties associated with the ore grades and reserves and commodity prices, and thus, fail to yield the truly optimized pit limits in any time horizon. In this paper, a new algorithm, MCSIMFNN, which overcomes these limitations is proposed and used to optimize open pit limits. The random field properties of the ore grade and reserves have been modelled using the modified conditional simulation based on the best linear unbiased estimation and local average subdivision techniques. Artificial neural networks are used to classify the blocks into classes based on their conditioned values. The error back propagation algorithm, in the neural networks, is used to optimize the pit limits by minimizing the desired and actual outputs error in a multi- layer perceptron under the pit wall slope constraints. The optimized pit value obtained by this algorithm has been compared to that from the Lerchs-Grossman's algorithm in a case study. The results are the same, but in random multivariable states, the MCSI MFNN algorithm is the most suitable for pit optimization. The method is also fast since it mimics the operation of the brain in the solution of the pit optimization problem.