Mining Optimization Laboratory

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1 Mining Optimization Laboratory Report Four 2011/2012 Directed by Hooman Askari-Nasab School of Mining and Petroleum Engineering Department of Civil & Environmental Engineering, University of Alberta, Edmonton, Alberta, CANADA

2 Askari-Nasab, Hooman (2012), Mining Optimization Laboratory (MOL) Report Four, MOL, University of Alberta, Edmonton, Canada, 340 Pages, ISBN: All rights reserved, all material in this report is, unless otherwise stated, the property of the Mining Optimization Laboratory (MOL). Reproduction or retransmission of the materials, in whole or in part, in any manner, without the prior written consent of the copyright holder, is a violation of copyright law. The report may be circulated and disposed at your discretion; however, the following copyright notice must be adhered to. Copyright 2012, Mining Optimization Laboratory, University of Alberta Mining Optimization Laboratory sponsors may utilize and disclose the report material and software within their organization with no prior permission of MOL. Contact information for requests for permission to reproduce or distribute materials available through this report is: Hooman Askari-Nasab, PhD, PEng Director of Mining Optimization Laboratory Assistant Professor of Mining Engineering Markin/CNRL NREF Building Department of Civil & Environmental Engineering University of Alberta, Edmonton, AB, Canada T6G 2W2 Phone: +1 (780) Fax: +1 (780) hooman@ualberta.ca Webpage: MOL Webpage: (i)

3 Executive Summary This year, we have prepared a hardcopy report including 15 papers, a CD-ROM containing all the papers in PDF format, Power Point presentations, and software source code. The prototype software code is documented and is available through HTML links in the appendix section of the papers online. We continue to update all the research results on the MOL webpage on the members section. Sponsors have access to current and past research results, publications, prototype software, and source code. Limited information is available to nonmembers, hopefully just enough for them to seriously consider membership. Let s review the contributions in the MOL Report Four (2011/2012) by considering some of the main contributors. Mohammad has continued working on development, implementation, and verification of clustering algorithms for block aggregation (paper 101). His latest achievements are in controlling cluster shapes including applying minimum cluster size, removing sharp corners, considering mining direction and creating clusters specialized for stratified deposits. The algorithms aggregate blocks into selective mining units based on a similarity index which can be defined based on rock-types, ore grades, block destinations and distance between blocks. Mohammad also has also improved the performance of the algorithm to decrease the time and memory required to run the algorithm. Clemens has focused on the gap problem generated by push backs derived from parameterization of the ultimate pit. The drawback of ultimate pit parameterization is that the push-back size may vary greatly. It is often the case that a small push-back is followed by a very large push-back. Clemens presented an alternative approach for developing push-backs. To create push-backs not exceeding a fixed overall tonnage and ore tonnage he developed an integer optimization model. Iteratively solving the model yields a series of push-backs. The research uses linear relaxation, problem size reduction, and two heuristics based on greedy search, neighborhood selection, and local search. The algorithm development is successfully followed by implementation of the algorithm as prototype software. Three mining case studies are carried out to investigate the practicality of the methodology. Hesam has been working on developing models for open pit mine production planning and scheduling. He has developed a hierarchical long-term and medium-term open pit mine production scheduling model (paper 103). Two mixed integer linear programming (MILP) models are applied for long-term and medium-term mine production scheduling in an iron ore mine. The mixed integer linear programming model of the long-term mine production scheduling maximizes the net present value of the mining operation subject to a number of technical constraints. The MILP formulation, developed for medium-term mine production scheduling, models open pit mines with multiple destinations, multiple elements, multiple stockpiles, and multiple ramps/routes. The objective of the medium-term planning is to minimize the total operational costs subject to technical constraints and logical constraints such as tracking the long-term plan. The proposed hierarchical model is then verified by the caw study. Behrang has been working on long-term mine planning in the presence of grade uncertainty. The optimality of an open pit production scheduling problem is affected dramatically by grade uncertainty. A mathematical programming formulation is presented to find a sequence in which ore and waste blocks should be removed from a predefined open pit outline and their respective destinations, over the life of mine, so that the net present value of the operation is maximized and the deviations from the annual target ore production is minimized in the presence of grade uncertainty. The results of the optimization model show that the extraction of high uncertain blocks is deferred to the later years of production. A penalty value is applied for any probable over and (ii)

4 under production. The main idea is that uncertainty costs money and should be deferred. The challenging question is the quantification of the cost of uncertainty. Yashar has worked on long-term block cave mine production scheduling using mixed integer linear programming (MILP). He has carried out mathematical programming formulations for block cave production scheduling for different level of resolutions. These levels include cluster level, darwpoint level and darwpoint and slice level. The first two levels are explained and compared in paper 105. To overcome the size problem of mathematical programming models and to generate a robust practical near-optimal schedule, his research has led to a multi-step method for long-term production scheduling of block caving (paper 106). A mixed integer linear programming (MILP) formulation is used for each step. The formulations are developed, implemented, and verified in the TOMLAB/CPLEX environment. The production scheduler aims at maximizing the net present value of the mining operation while the mine planner has control over the: development rate, vertical mining rate, lateral mining, dilution entry, mining capacity, maximum number of active drawpoints, cave draw strategies and advancement direction, and draw rate. The production scheduler defines: the opening and closing time of each drawpoint, the draw rate from each drawpoint, the number of new drawpoints that need to be constructed, and the sequence of extraction from the drawpoints to support a given production target. Elmira has focused on a truck-and-shovel simulation model to assist in utilizing these resources efficiently. Operation of trucks and shovels has a significant contribution to the overall operational costs in a mine. An efficient truck-and-shovel system creates reduction in hauling, operating, and maintenance costs. Elmira s paper presents a methodology for developing and implementing a simulation model to analyze the truck-and-shovel haulage system in open-pit mining with a link to the short-term plans. This approach guarantees that the optimum net present value obtained in longterm scheduling and short-term scheduling phases will be followed in operational plans as well. Macroscopic discrete event simulation models have been used so far in the industry and they prove to be a good tool to model this operation. But they miss one important aspect, i.e. the microscopic behavior of trucks while traveling. Shiv s paper (paper 302) concentrates on a model for capturing the real time behavior of trucks on the haul roads; taking into account the accelerations and decelerations of trucks based on gradient, turning angle, road conditions and platoon formations; which affects significantly the cycle times of the trucks. The objective of this research is: a) to develop a model which demonstrates the microscopic behavior of a mining transport system and to draw out a comparison with the existing software and real life mining transportation data and; b) extend this model to determine the optimal number of trucks (and shovels) in a given transportation system (considering the fact that increase in the number of trucks increases traffic delays on the common shared haul roads). A microscopic discrete event simulation model has been developed using the numerical equations of motion. The model has been verified against various desired characteristics of the transportation system incorporated into the model and also verified against Talpac software results. Eugene has worked on oil sands production scheduling and waste disposal planning. His current work is on coupling oil sands long term production planning and waste management using goal programming (paper 201). In oil sands mining, providing processable ore and tailings containment at the right time are the main drivers for profitability and sustainability. The recent Alberta Energy Resources Conservation Board Directive 074 requires oil sands waste disposal planning to be an integral part of mine planning. This requires an integrated strategy of directional mining and tailings dyke construction for in-pit and ex-pit tailings management. His work introduces a Mixed Integer Linear Goal Programming (MILGP) mine planning model to: a) determine the order and time of extraction of ore, dyke material and waste over the mine life that maximizes the operation s net present value; and b) determine dyke material destination that minimizes dyke construction cost. To implement an efficient MILGP model, an initial production schedule was generated and (iii)

5 used as an input for the optimization process. This reduced the size and solution time of the oil sands production scheduling and waste management optimization problem leading to the implementation of sustainable mining strategies. This includes an efficiently modeled pushback mining strategy. The model created value and a sustainable operation by generating a practical, smooth and uniform schedule for ore and dyke material. Mohammad Mahdi has been working towards integration of mine planning with tailings and reclamation plans in oil sands industry. Tailings is considered to be the main by-product of oil sands processing. Due to the noticeable amount of fresh and recycled water used in the process of bitumen extraction, huge volume of slurry is produced at the end point of the process. The amount of tailings produced is also important from environmental point of view. The ultimate goal of this research is to develop a multi-objective mine planning framework that will maximize the net present value of the mining operations while minimizing the reclamation material handling costs. The model includes capacity constraints for tailings facilities. The tailings calculation formula is retrieved from Suncor s process flow sheet. Some advanced assumptions, such as horizontal mining direction and pushback precedence, are included in the model as well. The integrated mixed integer linear programming (MILP) model is run with real case oil sand data set. The results show that the optimal mine plan meets tailings constraints and guarantees delivery of required reclamation material. Further steps of research are discussed at the end. Mohammad and Ebrahim have aimed at using Arena discrete event simulation software to study a processing plant and link the truck-shovel data to the final concentrate. A magnetic iron separation plant with various separation and comminution stages is considered and modeled in paper 303. The model illustrates how fluctuations in the plant feed and uncertainties in plant failures and operational conditions can affect the concentrate grade and tonnage. Various opportunities in using the simulation model and their modeling approaches are presented. Hooman Askari September 2012 (iv)

6 Annual Research Report Four Mining Optimization Laboratory (MOL) 2011/2012 Paper Page Title Table of Contents 100 Mine Planning & Production Scheduling Optimization Advances on block aggregation using hierarchical clustering, Mohammad Tabesh and Hooman Askari-Nasab An alternative approach to push-back design, Clemens Mieth and Hooman Askari- Nasab Hierarchical open-pit mine production scheduling optimization linking the strategic plan to monthly schedule, Hesameddin Eivazy and Hooman Askari- Nasab A mixed integer linear programming model for long-term mine planning in the presence of grade uncertainty, Behrang Koushavand, Hooman Askari-Nasab, and Clayton V. Deutsch An application of mixed integer linear programming for block cave production scheduling, Yashar Pourrahimian and Hooman Askari-Nasab Implementation of a multilevel mathematical programming formulation for block cave production planning, Yashar Pourrahimian and Hooman Askari-Nasab. 200 Oil Sands Mine and Tailings Planning Robust decision making: coupling oil sands mine and waste disposal planning, Eugene Ben-Awuah and Hooman Askari-Nasab An integrated model for oil sands long-term mine planning, tailings and reclamation plans, Mohammad Mahdi Badiozamani and Hooman Askari-Nasab. 300 Applications of Discrete Event Simulation in Mining Micro-simulation of mine haulage systems, Shiv Upadhyay and Hooman Askari- Nasab. (v)

7 Paper Page Title Verifying short-term production schedules using truck-shovel simulation, Elmira Torkamani and Hooman Askari-Nasab Linking mine production to milling and concentrate using discrete event simulation, Mohammad Tabesh, Ebrahim Azimi and Hooman Askari-Nasab. 400 Software Related Guidelines Guidelines for basic truck-shovel simulation modeling using Arena, Hooman Askari-Nasab and Mohammad Tabesh A guide to use resource sets and schedules in truck-shovel simulation in Arena, Hooman Askari-Nasab Truck-shovel simulation modeling failures, Hooman Askari-Nasab Resource classification guidelines in Gemcom Gems, Hooman Askari-Nasab. (vi)

8 Mining Optimization Laboratory (MOL) Sponsors: Newmont USA Limited E. Dry Creek Road Englwood, CO 80112, USA Contact: Xiaolin Wu BHP Billiton Innovation Pty Ltd (ABN ) 180 Lonsdale St Melbourne, Victoria, 3000 Contact: Peter M. Stone Maptek Pty Ltd. 165 S. Union Blvd. Suite 888 Lakewood, CO, USA Contact: Eric Gonzalez Mintec, Inc Homer St., Suite 400 Vancouver, BC, V6B 2Y5 Contact: : John C. Davies Suncor Energy Inc. (Oilsands) P.O. Box 4001, Tar Island Drive Fort McMurray, AB, Canada, T9H 3E3, Mine Planning & Projects Contact: Ross McElroy Shell Canada Limited Heavy Oil-Upstream Americas 400 4th Avenue S.W., PO Box 100, Station M, Calgary, Alberta T2P 2H5, Canada Contact: Jeffery Roberts (vii)

9 Mining Optimization Laboratory (MOL) Researchers / Graduate Students Following are researchers and students affiliated with Mining Optimization Laboratory in September Hooman Askari-Nasab Assistant Professor and Director of MOL 2. Kwame Awuah-Offei Assistant Professor of Mining Engineering 3. Mohammad Mahdi Badiozamani PhD Student /09 4. Eugene Ben-Awuah PhD Student /01 5. Yashar Pourrahimian PhD Student /09 6. Behrang Koushavand PhD Student (CCG) /09 7. Mohammad Tabesh PhD Student /09 8. Elmira Torkamani MSc Student /09 9. Shiv Prakash Upadhyay PhD Student / Clemens Mieth Msc Student /09 (viii)