This Case Shows Specific Expertise. Find the Companies with the Skills Your Project Demands!

You're viewing one of tens of thousands of real cases compiled on Many.dev. Each case demonstrates specific, tangible expertise.

But how do you find the company that possesses the exact skills and experience needed for your project? Forget generic filters!

Our unique AI system allows you to describe your project in your own words and instantly get a list of companies that have already successfully applied that precise expertise in similar projects.

Create a free account to unlock powerful AI-powered search and connect with companies whose expertise directly matches your project's requirements.

Smart Flotation Monitoring System Using Computer Vision

exposit.com

Manufacturing

Energy & natural resources

Current Challenges in Flotation Process Monitoring

Manual monitoring of flotation froth bubbles by operators is time-consuming, prone to human error, and lacks real-time data analysis. This limits process optimization, increases operational costs, and reduces competitiveness in the mining equipment market.

About the Client

A leading manufacturer of flotation machines for mining and processing industries with over a decade of experience.

Key Goals for the New Development

Automate visual tracking of flotation foam indicators (size, color, stability)
Enable real-time analysis of froth flotation efficiency
Provide actionable reports for process optimization
Reduce implementation risks through a clear MVP roadmap
Enhance client competitiveness via AI-driven solutions

Core System Capabilities

Real-time foam velocity and movement tracking
Bubble size distribution and average air bubble size calculation
Foam stability and brightness (greyscale) analysis
RGB color channel monitoring
Neural network model for data pattern recognition
Interactive statistics dashboard for operators
Integration with existing flotation machine systems

Technology Stack

Angular

Python

Node.js

Docker

PostgreSQL

WebSockets (Socket.IO)

System Integrations

Existing flotation machine control systems
Industrial camera hardware
Customer data platforms for mining operations

Performance and Scalability Requirements

Real-time processing with <500ms latency
High-accuracy neural network predictions (>95% reliability)
Secure data transmission between sensors and servers
Scalable architecture for multi-site deployment

Expected Business Outcomes

Automated monitoring will increase operational efficiency by 30%, reduce manual labor costs, and enable data-driven decision-making for mining clients. The solution will position the client as an innovator in smart mining equipment, creating a 15-20% competitive advantage in market differentiation.