Mining Optimization Laboratory (MOL)

MOL Report One - 2008/2009

Official repository version: University of Alberta ERA

ERA DOI: 10.7939/84191

Recommended citation DOI: Use the ERA DOI above when citing this report.

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Authors: Hooman Askari-Nasab

Institution: University of Alberta, Mining Optimization Laboratory (MOL)

ISBN: 978-1-55195-279-6 | ERA DOI: 10.7939/84191

Previous DOI / alternate version: 10.13140/RG.2.2.23950.37449

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About this report

This volume compiles 18 papers (335 pages) produced by MOL during 2008/2009. Individual chapters remain listed on the Publications page for direct access.

Suggested citation

Askari-Nasab, H. (2009). Mining Optimization Laboratory (MOL) - Report One, 2008/2009 University of Alberta, Mining Optimization Laboratory (MOL). https://doi.org/10.7939/84191

Rights and use

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.

This first report may be distributed to other potential sponsors for advertisement purposes. The report may be circulated and disposed at your discretion; however, the following copyright notice must be adhered to.

Mining Optimization Laboratory sponsors may utilize and disclose the report material and software within their organization with no prior permission of MOL.

Contact for permissions

Hooman Askari-Nasab, Ph.D., P.Eng.
Director of Mining Optimization Laboratory
Assistant Professor of Mining Engineering
3-044 Markin/CNRL NREF Building
Department of Civil & Environmental Engineering
University of Alberta, Edmonton, AB, Canada T6G 2W2
Phone: (780) 492-4053
Fax: (780) 492-0249
Email: hooman@ualberta.ca

Executive Summary

I am pleased to announce the release of the first Mining Optimization Laboratory (MOL) annual research report. MOL is an industrial affiliates program at the School of Mining and Petroleum Engineering at the University of Alberta. The program was launched in January 2009. I would like to thank the support of the founding sponsors, Newmont USA Limited and Suncor Energy Inc., in sponsoring this initiative.

It is anticipated that the value generated to sponsors by the outcome of research will create more industrial awareness and excitement to support the research group in the long run, with a focus on two major themes: (i) risk-based mine planning and design and (ii) simulation optimization of mining systems. The mission of the MOL research group is to (i) train highly qualified personnel and (ii) extend industry best-practice guidelines and standards in these areas. This report demonstrates the value generated to our sponsors through research and gives a sense of the quality of research and deliverables offered to potential sponsors.

As promised, research and training have been conducted and documented in two main categories: mine planning and design, and simulation optimization of mining systems. This report is a deliverable intended to rationalize sponsorship of MOL research. The students at MOL are just starting their programs and are going through a training phase by taking courses and working on mine planning projects. However, I am satisfied with the research results documented in this report and foresee a successful research group focused on far-sighted fundamental research addressing practical industry needs.

This report presents a number of interesting contributions toward developing the major components of a risk-based mine planning framework. Hooman worked on large-scale long-term open pit production scheduling using mixed integer linear programming (MILP) techniques, including capabilities to improve net present value while honoring technical constraints such as mining-fleet capacity, shovel limits, processing options, multiple processes, multiple elements, and blending and head-grade constraints for ore and deleterious materials.

To address the limitations of MILP models for large-scale mine production scheduling, the concept of mining-cuts was introduced into the formulations. Blocks within the same level or mining bench are grouped into clusters based on their attributes, spatial location, rock type, and grade distribution. These models were implemented in MATLAB and TOMLAB/CPLEX and demonstrated their ability to handle deterministic large-scale mine production problems and generate high-value practical schedules.

Another ongoing research direction involved mine planning using an intelligent open pit production simulator (IOPS), developed and implemented in Java and MATLAB and tested against Whittle software. Research was also carried out on quantifying the uncertainty transferred from geological modeling into mine planning through sequential Gaussian simulation and comparative scheduling analyses.

Additional contributions included cable shovel kinematics and dynamic modeling, asphalt mix design optimization, improved practical feasibility of scheduling patterns, documentation for short-term scheduling using Excel Solver, pit optimization and blending, and orebody modeling, optimization, and pit design of an oil sands deposit. The report also marks the arrival of new PhD students joining MOL in September 2009.

September 2009

Paper	Page	Title
100 Mine Planning and Design
101	8	Mixed integer linear programming formulations for open pit production scheduling, Hooman Askari-Nasab and Kwame Awuah-Offei.
102	39	Numerical modelling of the MILP formulation for open pit production scheduling, Hooman Askari-Nasab and Kwame Awuah-Offei.
103	63	Transfer of geological uncertainty into mine planning, Behrang Koushavand and Hooman Askari-Nasab.
104	84	Modeling variable pit slopes in the open pit production scheduling MILP formulation, Hooman Askari-Nasab and Kwame Awuah-Offei.
105	95	An agent based framework for open pit mine planning, Hooman Askari-Nasab and Kwame Awuah-Offei.
106	113	Open pit optimisation using discounted economic block values, Hooman Askari-Nasab and Kwame Awuah-Offei.
107	134	Open pit limits optimization using linear programming, Eugene Ben-Awuah and Hooman Askari-Nasab.
108	150	Optimization of blending process in mine production, Samira Kalantari and Hooman Askari-Nasab.
109	168	Optimizing block extraction sequence with MIP method and investigating the effect of road condition on truck cycle time, Yashar Pourrahimian and Hooman Askari-Nasab.
110	189	A review of open pit mine production scheduling and a guide for using Excel Solver in modeling MIP problems, Yashar Pourrahimian and Hooman Askari-Nasab.
111	207	McMurray oil sands pit design using Gemcom and Whittle, Samira Kalantari and Hooman Askari-Nasab.
112	220	Orebody modeling, optimization and pit design using GEMS and Whittle, Eugene Ben-Awuah.
200 Optimization & Simulation
201	236	Simulation of a crushing process, Eugene Ben-Awuah and Hooman Askari-Nasab.
202	249	Simulation of mine production using MATLAB and AweSim software, Samira Kalantari and Hooman Askari-Nasab.
203	270	Cable shovel dynamic simulation application in pre-blasting decision-making, Kwame Awuah-Offei and Hooman Askari-Nasab.
204	286	Reliability analysis of truck-shovel systems in mining, Kwame Awuah-Offei and Hooman Askari-Nasab.
205	303	Asphalt mix design optimization for efficient plant management, Kwame Awuah-Offei and Hooman Askari-Nasab.
206	321	Life cycle assessment of belt conveyor and truck haulage systems in an open pit mine, Kwame Awuah-Offei, David Checkel, and Hooman Askari-Nasab.
300 Software Related
301	331	Guidelines for using TOMLAB on Linux clusters, Hooman Askari-Nasab.