Mining Goes Digital – Mueller et al. (Eds)
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
335
Underground mine planning optimization process to improve
values and reduce risks
Hongliang (Henry) Wang
Newmont Mining Corporation, Denver, USA
ABSTRACT: Underground mine planning and optimization has been dramatically
improved over the last decade, thanks to a group of innovative tools that have transformed
the process. With a case study, this paper is to demonstrate a complete and practical workflow
aiming to improve values and reduce risks. The tools applied for this case study include: Stope
Optimizer (MSO), Level Designer (PUNO) for rapid mine designs, Hill of Value Model
(HoV) for Data Analysis and Strategic Mine Planning Optimization, Schedule Optimization
Tool (SOT) and OMP for schedule optimization with multiple layers of constraints.
1 INTRODUCTION
The lack of useful underground mine planning and optimization tools has changed over the
last decade or so, thanks to a group of innovative tools that have been developed by researchers,
mining software vendors and mining companies
[1]
. With modern mine design software, mining
engineers are moving away from manual digitizing to using automatic design tools, and the
majority of the sequence and scheduling can be performed by software with robust rules and
logics. Optimization algorithm with fast computer is capable of handling strategic mine planning and very large and complicated underground scheduling problems
[2]
. However, the whole
underground mine planning process, including design, sequence, scheduling and optimization,
is still a very time-consuming process, even with the latest tools.
There are challenges between the objectives, such as improving project values, including
Net Present Value (NPV), Free Cash Flow (FCF), Internal Rate of Return (IRR), etc., and
reducing risks through the validation of operability and practicability of a mining project.
Very few literatures have explained how to balance between improving values (optimization)
and reducing risks (practicality). And cross the mining industry, there are still misperceptions, inconsistences and ambiguities in regards to what exactly the goal of mine planning
optimization is, at various stages of the project or life of the mine.
Underground mine planning and project evaluation processes, as illustrated by the following chart (Figure 1) in the simplest fashion, including studies of the ‘rock’ – resource and its
reliability and uncertainty, ‘ore’ – economical part of the rock (above certain cut-off grade
and ‘makes money’), and ‘value’ – how to make the most money.
This workflow above involves many iterations, with increasing definition of the resource
model and updates of the mine plans. A large amount of data can be produced with modern
computer and mine planning software. ‘Big Data’ is great for data analysis and to evaluate
multiple options and scenarios, but also requires robust data analysis and powerful algorithms to process the data and identify the best feasible mine plans.
The following sections will show a complete and practical workflow that has improved
values and reduced risks for an underground precious metal project. It involved inputs from
many other stakeholders, including geologists, resource modelers, metallurgists, open pit
engineers, project managers and mine operations. It is believed that some of lessons learned
and best practices can be shared by other projects, however, every deposit and operation
is also unique, so we cannot simply copy the mine planning process from one project to
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
335
Underground mine planning optimization process to improve
values and reduce risks
Hongliang (Henry) Wang
Newmont Mining Corporation, Denver, USA
ABSTRACT: Underground mine planning and optimization has been dramatically
improved over the last decade, thanks to a group of innovative tools that have transformed
the process. With a case study, this paper is to demonstrate a complete and practical workflow
aiming to improve values and reduce risks. The tools applied for this case study include: Stope
Optimizer (MSO), Level Designer (PUNO) for rapid mine designs, Hill of Value Model
(HoV) for Data Analysis and Strategic Mine Planning Optimization, Schedule Optimization
Tool (SOT) and OMP for schedule optimization with multiple layers of constraints.
1 INTRODUCTION
The lack of useful underground mine planning and optimization tools has changed over the
last decade or so, thanks to a group of innovative tools that have been developed by researchers,
mining software vendors and mining companies
[1]
. With modern mine design software, mining
engineers are moving away from manual digitizing to using automatic design tools, and the
majority of the sequence and scheduling can be performed by software with robust rules and
logics. Optimization algorithm with fast computer is capable of handling strategic mine planning and very large and complicated underground scheduling problems
[2]
. However, the whole
underground mine planning process, including design, sequence, scheduling and optimization,
is still a very time-consuming process, even with the latest tools.
There are challenges between the objectives, such as improving project values, including
Net Present Value (NPV), Free Cash Flow (FCF), Internal Rate of Return (IRR), etc., and
reducing risks through the validation of operability and practicability of a mining project.
Very few literatures have explained how to balance between improving values (optimization)
and reducing risks (practicality). And cross the mining industry, there are still misperceptions, inconsistences and ambiguities in regards to what exactly the goal of mine planning
optimization is, at various stages of the project or life of the mine.
Underground mine planning and project evaluation processes, as illustrated by the following chart (Figure 1) in the simplest fashion, including studies of the ‘rock’ – resource and its
reliability and uncertainty, ‘ore’ – economical part of the rock (above certain cut-off grade
and ‘makes money’), and ‘value’ – how to make the most money.
This workflow above involves many iterations, with increasing definition of the resource
model and updates of the mine plans. A large amount of data can be produced with modern
computer and mine planning software. ‘Big Data’ is great for data analysis and to evaluate
multiple options and scenarios, but also requires robust data analysis and powerful algorithms to process the data and identify the best feasible mine plans.
The following sections will show a complete and practical workflow that has improved
values and reduced risks for an underground precious metal project. It involved inputs from
many other stakeholders, including geologists, resource modelers, metallurgists, open pit
engineers, project managers and mine operations. It is believed that some of lessons learned
and best practices can be shared by other projects, however, every deposit and operation
is also unique, so we cannot simply copy the mine planning process from one project to