Coal Mining

Challenge

Seriti is a mining company in South Africa that uses continuous miners to fetch coal from underground mines using a particular configuration for the mining process. The mining strategy requires defining very particular road sizes to extract coal effectively. While continuous miners extract the coal, a number of shuttle cars collect and transfer it to a feeder breaker positioned strategically. Since there are more than 2 shuttles, a change-out point is defined to wait for other shuttles to position themselves in safe spots to avoid collisions.

The mining strategy has been the same for a very long time and has never been tested to verify its efficiency. Experts have had the monopoly of knowledge to decide where to locate change-out points, the order of extraction, and the position of the feeder breaker. Since this process takes several weeks, it's virtually impossible to try new strategies in production — this is where simulations play a significant role, allowing millions of tests to define an optimized strategy without cost. Saving days of work has a tremendous effect on costs and extraction rates.

Solution

An Agent-Based model was built that was completely flexible on the configuration of roads, positioning of different actors, and could export all raw data to Excel. The model consisted of four different levels:

• Configuration of the system
• Simulation of the mining process using agents
• Engine to calculate optimal path planning to avoid collisions
• Data output and KPIs

The model also included definition of shifts, maintenance, breakdowns, downtimes, and resources.

Outcome

The model allowed the company to determine a pseudo-optimal strategy to mine coal in order to reduce costs, movements, and resource utilization while maximizing extraction rate.

The uniqueness of this project was the utilization of AnyLogic to configure very complex visualization patterns and generate collision-free movements using state-of-the-art algorithms. This approach gives the mining company freedom to generate new untested configurations without the cost of testing in production. It also helps in project planning since the model predicts the amount of time the project will take.

Project Features

• Industry: Mining
• Model: Agent-Based
• Duration: 2.5 months