Conventional vs. Parametric Mine Design
During the mine planning and design process, it is critical to establish a living design model and create an intelligent workflow that updates as objects and inputs change. The design of the know-how model that quickly allows for the analysis of a multitude of scenarios can reduce the financial risk of a project.
Designing parametrically can not only reduce the financial risks but can also significantly reduce the designer’s or planner’s reliance on the manual, time-consuming CAD line design process.
Standard General Mine Planning (GMP) products today require manual work that is slow and labour-intensive. However, when integrated with automated parametric products, the mine design and planning process ensures faster execution, at less of a cost, and with improved accuracy.
This approach generates multiple design options over a short time, which incorporates testing of new ideas, strengthens decision-making by using confident project results and reduces risks within a project.
Characteristics of Conventional Mine Design
Underground mining is a good example of how the scale of deposits and engineering complexity expands as mines go deeper. Orebody geometry, geotechnical considerations, and planning get more complicated as the size of a mine increases. This makes optimization for technical workflows all the more important.
Mine designers and planners often generate carefully considered multiple design options. Evolving production locations constantly require frequent design updates. Costing and processing, logistics and regulatory changes can also force changes to the design. Each change can require multiple design iterations and often these changes lead to large segments of a design having to be redone manually.
Legacy design workflows based on manual, time-consuming CAD/line passes can take from weeks to months of work. Often these designs are updated as new information becomes available, which requires a redesign of the large portions of the mine development.
With this traditional approach, it is often difficult to generate multiple scenarios for study and sustainable evaluation.
Endless Iterations
For example, say you want to design a dual haulage/return airway. To come up with a single design, you would need to calculate size and shape, grade and direction, distance apart, the distance between connections for rail-bound or trackless conditions, distance below or offset from an ore body, and orientation at fault intersections. If the new equipment selected does not match the initial design specifications, the designer will have to re-do the entire design to meet the new design requirements.
But what if you had an automated approach connected to a CAD system that renders the mathematics of those parameters to create a design? Every change in the values of those parameters instantly changes the model. Instead of the engineer calculating values of individual parameters to produce a single design, the parameters themselves generate a new model instantly simply by plugging in new parameter values.
Parametric Design
Parametric design does not produce a solution as much as generate a family of possible outcomes through dynamic automation.
Parametric modeling can be divided into two main types:
- Propagation-based systems, in which algorithms result in final shapes that are unknown based on initial parametric inputs, through a dataflow model.
- Constraint systems, in which the final constraints are set and algorithms used to define fundamentals such as structures and material use.
The "form-finding" processes, which are implemented through propagation-based systems, optimizes certain design goals against a set of design constraints, meaning the final form of the designed object is "found" based on these constraints.
Living Model
This model-based approach incorporates traditional CAD functions but differs by adding links between objects and parameters. Users can also create templates by combing a series of functions and parameters, to speed up the time needed to design repetitive tasks. Either the templates can be deployed manually or they can be automated through scripting. This creates a “living” model where design changes made in a localized area update the global mine design.
This approach allows for quick analysis of different design criteria based on input parameters and facilitates collaboration when data and updates from other areas of operation are hosted on a digital Platform
Challenge of Complexity
Parametric design increases speed and accuracy, which sharply cuts design cycle times -- weeks or months of design work collapsed into minutes or hours. Consequently, engineers can develop many more design iterations to evaluate, and they have greater flexibility to explore more options. In addition, this approach captures new design knowledge into templates instead of in the brains of highly talented design engineers steeped in the standards of legacy approaches.
However, it is worth mentioning that building complex parametric design models takes time. It requires learning new software, and refining computational thinking to improve understanding of such concepts as pattern recognition by using algorithms, and abstractions. Once comfortable with this new mine design approach, designs can be created much faster than with traditional GMP products. It also adds the benefit of the parametric nature of the design, allowing for flexibility of often-unforeseen changes that may affect the initial design.
The next article of this series, discusses how to apply simulation to parametric mine design.
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.
Related posts
