Simulation Using Parametric Mine Design

Simulation is the imitation of operations within a real-world process or system. Generally, the model represents the key behaviors or objectives of that process, and as simulation progresses over time, the model gathers more knowledge and better represents the process or system.  Mining projects are vastly complicated, expensive and risky ventures, so being able to simulate everything from the mine design to the material movement allows engineers and key stakeholders to understand how the complex system works and is critical in de-risking a project.

Introduction to Simulation 

Many industries use simulation in their planning process.  Aerospace uses it everywhere from pilot training to aircraft design.  Automotive uses simulation from vehicle conception to design and manufacturing.  Yet traditionally in mining, our simulations are often built from silos of data, software packages and processes.  Automating these individual processes within a single model allows planners and engineers to explore more scenarios and better understand optimal design parameters and model drivers and constraints.

Parametric modelling is a key component within the simulation process that allows for the evaluation of multiple designs by automating the manual and iterative work done by designers and engineers.  The simulation model contains not only the actual structure being designed, but also a conceptual structure used to guide variations and the resulting outputs and objectives for comparison.  

Simulation lets the engineer compare the original design configuration with a larger spectrum of data generated by means of parametric simulation.  Regression models are trained first on simulation data and then progressively calibrated on measured data during a certain monitoring period.  This process has two fundamental objectives: (1) evaluating the robustness of design phase performance analysis through parametric simulation to detect potentially critical assumptions, and (2) maintaining a continuity with operation phase performance with feed-back from measured data.

Designing a Simulation

Simulation helps engineers understand and improve complex systems, because it creates a set of procedures to systematically test a hypothesis.  To do that, mine design engineers must have a good understanding of the process, the pertinent variables in that process and how they are related.  Engineers can better understand their causal relationships by manipulating one or more independent variables and measuring their effect on one or more dependent variables.

There are five basic steps in designing a simulation.

1. The first step is to write a specific, testable hypothesis or objective of the simulation model.  This step can be applied to very limited goals or developed for broad strategic mine planning objectives that fully integrate geology, design, planning and scheduling applications.
2. The second step is to consider the variables and how they are related.  Since parametric design automates the propagation of effects as model inputs change, new designs and schedules are generated with each new data input.
3. The third step entails defining experimental treatments to manipulate independent variables.  Here we can set thresholds for parameters or assign weightings to the output objectives to rank the resulting simulations.
4. The fourth step is to analyze and interpret the results of the simulation.
5. Finally, the engineer must use the analysis of the previous step to update the model and any assumptions for future simulations.

The objective of the simulation is to end up with a reflective and predictive model of the system or processes being analyzed so that decisions can be made swiftly, accurately and with confidence.

Applying Simulation in Parametric Design

Simulation using parametric modelling can be developed and used instead of conventional mine models to design mining phases that consider such unexpected variations and uncertainties as metal content available in a mineral deposit and shifting commodity prices.

Using various simulation techniques such as a Design of Experiments (DoE), the parametric design can generate multiple scenarios for orebody interpretation, various mining methods, recovery sequence, and productive capacity.  Various simulation techniques can be used to study multiple designs, from mine electrification to mine optimization.

In the illustration below (see graphic), a DoE is used to update the mine design for a block cave to see the effect the different mine design has on various Key Performance Indicators (KPI’s)  such as Internal Rate of Return (IRR), Net Present Value (NPV) and overall grades/tonnes. Using process automation applications, we can integrate various tools to simulate the design and scheduling.  In this example, we created a simulation that used a DoE to modify the parameters of the mine design, such as tunnel spacing, azimuth and draw bell spacing to create the output design.  Then the simulation uses the newly created draw point locations from the output design to create the production schedule of the block cave, which would determine the reserves, tonnes, grade and schedule of the mine.  The DoE collects all the output data of the various scenarios so the engineer can quickly evaluate the effect the various input parameters had on KPI’s such as IRR and NPV.

A similar concept could be applied to different processes or mine designs.  For example, by changing various parameters, a model could calculate total design length as an output used to generate a cost for the design and to compare alternatives.

Complexity Managed Efficiently

In any given application, there could be an unlimited range of possible strategies for modeling a structure or process, but that does not guarantee the best solution.  Embedding parametric design within mine simulations helps engineers narrow their analyses to find optimal solutions.

The block cave example above is a good one to consider in the context of parametric design because the development phase of a block cave mine, which is more capital-intensive than other mining methods, can take up to several years of advance work before any production.  For this reason, it is imperative for operations to de-risk their projects by accurately simulating the systems and processes involved, to understand optimal design parameters, model drivers and constraints.

Simulation is not just limited to the caving example above; the same model-based approach combined with parametric design can be used across various mining methods and processes to efficiently generate multiple scenarios to increase the understanding of various inputs and optimize the value of a project.

The next article in this series looks at a specific use case that addresses unconventional problems with a parametric design approach. 

 Christina LUDWICKI is a Mining Industry Process Consultant at Dassault Systèmes GEOVIA with 15 years of experience in Industry and Consulting. Christina holds a BEng in Mining Engineering from Dalhousie University. Throughout her career, she has worked in various aspects and methods within the mining industry, from mine planning in narrow vein gold mines, to feasibility studies of large Block Caves and Sub-level Caves. Christina is now focused on developing cross brand synergies across the DS portfolio to deliver maximum value to the mining industry.

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