SOFEN MINING TECHNOLOGIES
Connect assets, data and decisions.
Maintenance, inspection, traceability and performance shaped around African mining operations.
Discuss a project
Intelligent mining dispatch
A SOFEN AI solution supporting haul-truck assignment decisions in open-pit mines. The prototype uses Machine Learning to estimate mining cycle times. The developed foundation includes data preparation, a LightGBM predictive model and an input interface displaying an estimated cycle time in minutes. It provides an initial decision-support component for dispatch teams.
Validation and development
The next step is to evaluate predictions on independent data, then explore dynamic haul-truck assignment. Reducing waiting times and improving productivity are objectives to measure with a mining partner. Presented by SOFEN, based on work by Eric TSHIBANGU under the supervision of Professor Blaise FYAMA. Commercial scope and on-site validation are to be defined with partners.
Equipment availability
Structure intervention histories and reliability indicators to prioritise maintenance. Predictive maintenance remains a development area requiring qualified data and field evaluation.
Inspection and HSE compliance
Connect inspections, findings, certificates and due dates. Traceability helps document decisions; it does not replace physical inspections or applicable obligations.
Operational performance
Consolidate production, availability, energy and cost data. Leaching and process optimisation remain exploratory areas to assess with specialists.
Variable connectivity
Remote sites require lightweight interfaces and explicit data exchange rules. Offline operation of mining software remains a requirement to study with a pilot partner; it is not presented as available.