3D Bulk Material Analysis for Pellet Size Distribution
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Solution Overview
Problem
Existing bulk material analysis methods, such as sieve analysis and image-based techniques, are complex, time-consuming, and unreliable, especially in industrial environments, particularly for determining the size distribution of metal ore pellets, which can affect the efficiency of direct reduction furnaces.
Innovation Solution
A 3D data set analysis method that reconstructs individual bulk material bodies virtually, using geometric fitting and least squares matrix calculations to determine parameters like diameter and out-of-roundness, enabling robust and efficient characterization of bulk materials even under poor lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sieve analysis is used to determine size distribution, then measurement accuracy is improved, but analysis time and complexity increase
Solution Approach 1:
The patent replaces the mechanical sieve analysis system with an optical measurement system using a camera and image processing algorithms. Instead of physically sieving bulk material through mechanical screens, the system captures images of individual particles and uses computer vision to determine size distribution, thereby eliminating the time-consuming mechanical process while maintaining measurement accuracy.
Solution Approach 2:
The patent creates digital copies (images) of the bulk material particles and analyzes these copies instead of the physical particles themselves. By capturing visual representations and processing them through algorithms, the system determines size distribution without requiring physical manipulation or sieving of the actual material, significantly reducing analysis time.
2Loss of time
If digital image processing is used to analyze bulk material, then analysis time is reduced, but reliability deteriorates under poor lighting conditions
Solution Approach 1:
The patent transitions from two-dimensional image analysis to three-dimensional reconstruction of particles. By capturing images from multiple angles or using stereo vision, the system builds 3D models of particles, enabling more reliable size and shape measurements that are less sensitive to lighting variations and viewing angles, thereby improving reliability while maintaining fast analysis times.
Solution Approach 2:
The patent employs advanced image processing parameters and algorithms that are robust to lighting variations. By transforming image data through multiple processing steps including contrast enhancement, noise filtering, and adaptive thresholding, the system maintains reliable particle identification and measurement under varying lighting conditions while preserving rapid analysis capability.
3Productivity
If image-based analysis is used in industrial environments, then productivity is improved, but measurement precision deteriorates due to obscured bulk solids
Solution Approach 1:
The patent applies segmentation algorithms that can identify and separate individual particles even when they are partially obscured or overlapping in the image. By using advanced image processing techniques such as edge detection, contour analysis, and machine learning-based segmentation, the system accurately measures particle sizes in dense bulk material configurations, maintaining precision while enabling high-speed industrial analysis.
Data Source
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AI summary
The invention relates to a method (100) and a device (20) for bulk material analysis, as well as to a bulk material production system (10), and a computer program product. According to the invention, individual bulk material bodies (16) of a bulk material (12) are identified (S2) in a 3D data set (14). For each identified bulk material body (16), a reconstructive calculation is carried out (S3), in which the bulk material body (16) is reconstructed on the basis of data (D) from the 3D data set (14) associated with the bulk material body (16). A bulk material characteristic (C) is then determined (S4) on the basis of at least one parameter value obtained in the reconstructive calculations, which characterizes the respective identified bulk material body (16).