Hypermine offers a laser-based hyperspectral sensing system that provides real-time mineralogy characterization at the mine site. This technology enables truck load resolution decision-making, improving ore grade control and operational efficiency by up to 100x compared to traditional block models.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional mining operations rely on block models with low resolution for decision-making, leading to suboptimal ore grade control and reduced operational efficiency. This lack of granular, real-time data at the point of extraction hinders the ability to maximize mineral yield and optimize resource utilization.
Solution
Hypermine provides a proprietary laser-based hyperspectral sensing system for real-time mineralogy characterization directly at the mine site during Run-of-Mine operations. This technology enables decision-making at truck load resolution, offering up to 100x greater accuracy compared to traditional block models. The system leverages AI-powered analytics to identify minerals under diverse environmental conditions, including dust, rain, and varying light levels, up to 50 meters away. By delivering precise mineralogical data at the earliest stage of the mining process, Hypermine empowers operations to significantly boost mine yield and enhance overall efficiency.
Target Audience
Hypermine targets mining operations seeking to optimize resource extraction and improve operational efficiency through advanced, real-time mineralogical data.
Features
- Proprietary laser technology for hyperspectral mineral detection up to 50 meters
- Real-time mineralogy characterization under all environmental conditions (darkness, sunlight, rain, mist, dust)
- AI-based analytics for rapid and reliable mineral identification using spectral signatures
- Application for muck pile scanning, truck load scanning, and shovel load scanning
- Enables decision-making at truck load resolution (100-400 tons) for enhanced ore grade control
- 100x improvement in decision accuracy compared to traditional block models