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Permanent link (DOI): https://doi.org/10.7939/R3N01050C

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Aggregation and Mathematical Programming for Long-Term Open Pit Production Planning Open Access

Descriptions

Other title
Subject/Keyword
Clustering
Mining aggregates
Long-term open pit production planning
Mixed integer linear programming
LTOPP
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Tabesh, Mohammad
Supervisor and department
Hooman Askari-Nasab
Examining committee member and department
Clayton Deutsch (Civil and Environemntal Engineering)
Hooman Askari-Nasab (Civil and Environemntal Engineering)
John Doucette (Mechanical Engineering)
Yashar Pourrahimian (Civil and Environemntal Engineering)
Wei Victor Liu (Civil and Environemntal Engineering)
Michel Gamache (Department of Mathematical and Industrial Engineering, Polytechnique Montréal)
Department
Department of Civil and Environmental Engineering
Specialization
Mining Engineering
Date accepted
2015-09-23T13:19:16Z
Graduation date
2015-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
The objective of this thesis is to develop, implement and verify a theoretical framework based upon aggregation and mathematical programming for solving the long-term open pit production planning problem. The goal is to closely estimate the maximum net present value of the operation by providing an optimum and practical mining, processing and stockpiling schedule for the open pit mining operation while respecting the technical and operational constraints. As stated by many researchers in the area and illustrated in the forth chapter of this thesis, using blocks as units of planning results in over-estimation of the operation’s profitability and flexibility. Thus, we introduced a clustering algorithm along with a mathematical formulation that can result in good production plans that result in high NPV, are practical and do not under- or over-estimate the value of the operation. In this thesis, we introduced, implemented and verified a specifically-designed clustering technique based on agglomerative hierarchical clustering, in order to aggregate blocks into mining-units that are homogenous in rock type and grade, and have mineable shape and size. We designed the algorithm, developed the codes and implemented and tested the algorithm on small test datasets and large real-size deposits to evaluate the performance of the algorithm. We showed that we can balance the clustering control parameters to obtain clusters of blocks aligned with the clustering purpose such as long-term planning units, ore polygons and blast patterns. Moreover, we formulated, implemented and verified a mixed integer linear programming model, for long-term open pit production planning problem, which can use two different sets of units for making mining and processing decisions. Our model is able to simultaneously determine the optimum stockpiling strategy and the optimum mining and processing schedule in reasonable processing time. We implemented our model on a small test dataset as well as real-size deposits to understand the effects of using different units of planning for making mining and processing decisions. We showed that we can obtain practical and optimum production schedules for real-size open pit mines in a reasonable time by benefiting from the clustering technique we introduced. Moreover, we showed how incorporating stockpile optimization in long-term production scheduling can increase the net present value of the operation. We benchmarked our model against commercial scheduling software to illustrate the flexibility and accuracy of our planning approach. The main contributions of this thesis to the mining body of knowledge are (i) introducing a clustering algorithm that creates aggregates of blocks homogenous in rock type and grade, with controlled shape and size, and respects the mining direction as well as other constraints and boundaries, (ii) introducing an MILP formulation for the long-term open pit production planning problem, with dynamic cut-off grade, that maximizes the net present value of the mine, by using two different units for making mining and processing decisions, while respecting operational and technical constraints, and (iii) incorporating stockpiling in the long-term scheduling to simultaneously optimize the mining, processing and stockpiling strategies.
Language
English
DOI
doi:10.7939/R3N01050C
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Tabesh, M., & Askari-Nasab, H. (2011). Two-stage clustering algorithm for block aggregation in open pit mines. Transactions of the Institutions of Mining and Metallurgy, Section A: Mining Technology, 120(3), 158-169.Tabesh, M., & Askari-Nasab, H. (2013). Automatic Creation of Mining Polygons using Hierarchical Clustering Techniques. Journal of Mining Science, 49(3), 426-439.Tabesh, M., Mieth, C., & Askari-Nasab, H. (2014). A Multi-Step Approach To Long-Term Open-Pit Production Planning. International Journal of Mining and Mineral Engineering, 5(4), 273-298.

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