Real-time Foam Layer Thickness Measurement powered by Depth Estimation

Based on Patent Research | CN-117953033-A (2024)

Achieving precise control in mineral flotation processes is challenged by the difficult real-time measurement of foam layer thickness. Current manual monitoring methods are inefficient and delay responses to process changes, affecting mineral recovery. However, depth estimation, a computer vision technique, offers a robust non-contact solution. It precisely measures foam thickness by determining depth from image data. This enables automated, real-time insights, leading to improved process control, faster responses, and enhanced mineral recovery.

SOLUTION OVERVIEW

Reimagining Foam Monitoring with AI Thickness

In Mining and Quarrying, precise mineral flotation control benefits significantly from Depth Estimation technology. This computer vision technique directly addresses the challenge of measuring foam layer thickness in real time. It operates by capturing image data from flotation cells, then advanced algorithms, including depth ranging neural networks, process this visual information to precisely determine the distance to the foam surface. This generates accurate, non-contact measurements, providing continuous insights into the critical processing environment.

The practical application of Depth Estimation allows for seamless automation of process monitoring, integrating readily into existing control systems without disrupting operations. Much like how a modern surveyor uses advanced tools to map terrain details without physical contact, this technology accurately maps the foam surface, enabling immediate and precise adjustments to flotation parameters. This capability leads to more efficient resource utilization and supports enhanced decision-making, ultimately contributing to improved mineral recovery and overall operational stability within the processing plant.

HOW IT WORKS

Analyzing Flotation Images for Foam Thickness

Capturing Flotation Cell Imagery

Specialized cameras capture continuous, high-resolution visual data from the mineral flotation cells. This binocular video provides the essential raw input, allowing the system to observe the dynamic foam surface in real time. The precise visual information is critical for subsequent analysis.

Processing Visual Information

A sophisticated depth ranging neural network then processes this captured image data. It meticulously extracts intricate visual features and patterns present on the foam surface, recognizing subtle variations. This initial processing step is crucial for understanding the visual scene.

Calculating Precise Foam Depth

Based on the extracted image features, the system constructs a detailed parallax map. This map enables the precise calculation of the distance to the foam surface, providing accurate, non-contact measurements of the foam layer thickness. These measurements are fundamental for process control.

Delivering Real-time Process Insights

The continuously calculated foam thickness measurements are immediately relayed to the control systems. This provides real-time, actionable insights into the dynamic flotation process, enabling prompt, automated adjustments to optimize mineral recovery. Ultimately, this leads to enhanced operational stability.

Potential Benefits

Maximize Mineral Recovery

Real-time foam thickness data enables precise adjustments to flotation parameters. This optimizes the process, significantly increasing valuable mineral extraction from the ore.

Real-time Process Optimization

The system provides continuous, accurate insights into foam layer dynamics. This allows for immediate, data-driven adjustments, ensuring peak process efficiency.

Enhance Operational Efficiency

Automating foam thickness measurement reduces manual labor and response times. This streamlines operations, leading to more efficient resource utilization and plant productivity.

Accurate Non-Contact Monitoring

Depth Estimation precisely measures foam thickness without physical contact. This eliminates manual inaccuracies, providing reliable data for consistent process control.

Implementation

1 Install Vision Hardware. Mount specialized binocular cameras around flotation cells. Ensure stable power and network connectivity for data transmission.

2 Configure Data Capture. Set up cameras for continuous, high-resolution visual data acquisition. Establish secure data streaming protocols.

3 Deploy AI Model. Implement the depth ranging neural network on appropriate computing infrastructure. Configure its initial processing parameters.

4 Calibrate and Validate. Calibrate the system for specific flotation cell conditions. Validate depth measurements for accuracy and reliability.

5 Integrate Control System. Connect real-time foam thickness outputs to the plant's existing control system, enabling automated parameter adjustments.

Source: Analysis based on Patent CN-117953033-A "Real-time non-contact foam layer thickness measuring method and system based on binocular vision" (Filed: April 2024).