Intelligent Computer Vision System for Flotation Process Monitoring and Optimization

Flotation processes in mineral mining rely heavily on visual indicators monitored by experienced operators, such as bubble size, foam stability, brightness, and color distribution. This manual approach introduces variability, delays, and limits real-time decision-making, potentially impacting process efficiency and product quality. There is a need for automated, real-time monitoring solutions to improve process consistency, reduce operational costs, and gain a competitive edge in flotation equipment offerings.

A mid-to-large scale mining equipment manufacturer seeking to enhance flotation process control with automated monitoring solutions.

• Develop an intelligent system capable of real-time analysis of flotation foam characteristics using computer vision techniques.
• Automate the extraction and measurement of key indicators such as foam brightness, velocity, bubble size distribution, stability, and color channels.
• Create a user-friendly analytics dashboard to display real-time data, statistics, and reports for process operators.
• Implement scalable data collection, labeling, and neural network training workflows to optimize system accuracy.
• Integrate the monitoring system seamlessly with existing flotation equipment and control software to enable operational automation and decision support.
• Reduce operator dependency and variability in flotation process monitoring, thereby enhancing productivity and process reliability.
• Provide a clear development roadmap to facilitate MVP testing, deployment, and ongoing improvements.

• High-resolution camera setup for continuous visual data acquisition of flotation surface.
• Algorithms to determine brightness (grey scale), RGB color channels, foam velocity projection, and bubble size distribution.
• Measurement of foam stability and fluctuation over time.
• Automated data labeling and dataset creation for neural network training.
• An internal analytics dashboard to display statistical summaries, real-time indicators, and trend reports.
• APIs for seamless integration with existing equipment control and monitoring systems.
• Data storage solutions that support scalable dataset growth and model training workflows.

• Existing flotation equipment control systems for seamless data exchange
• Real-time data collection modules from cameras and sensors
• Internal enterprise data management systems for report generation

• High system availability with at least 99.5% uptime to support continuous monitoring
• Real-time processing latency under 1 second for visual data analysis results
• Scalability to support multiple flotation units simultaneously
• Data security standards to protect proprietary process data
• Ease of maintenance and system updates to accommodate ongoing improvements

Implementation of the automated flotation monitoring system is expected to boost process productivity through real-time indicators and decision support. It aims to reduce dependency on manual visual inspection, lower operational costs, and improve process consistency. The project intends to enable faster, more accurate process adjustments, resulting in higher concentrate yields and improved product quality. Additionally, establishing a clear development roadmap and neural network training workflow will mitigate implementation risks and foster ongoing system enhancements.