OreOpt utilizes open-source AI and machine learning tools to optimize ore grade extraction in mining operations. It analyzes real-time data from drilling and blasting processes to maximize ore yield, improve resource utilization, and reduce waste.
Overview
Business Challenge
Mining companies struggle with maximizing ore yield while minimizing waste. Traditional methods often result in suboptimal resource utilization and increased operational costs.
Business Objective
The primary goal of OreOpt is to optimize ore grade extraction and improve resource utilization. By leveraging real-time data and machine learning, it aims to increase ore yield and reduce waste.
Solution
OreOpt integrates several advanced technologies and features:
- Real-Time Data Analysis: Uses Apache Kafka for streaming data from drilling and blasting operations.
- Machine Learning Models: Employs scikit-learn and TensorFlow to optimize ore grade extraction.
- Resource Utilization: Analyzes data to improve resource allocation and minimize waste.
Business Value
Implementing OreOpt leads to several benefits:
- Increased Yield: Improves ore yield by up to 25%.
- Cost Savings: Reduces operational costs by optimizing resource utilization.
- Efficiency: Enhances overall operational efficiency and reduces waste.
