Aggregate Particle Characterization for High-Throughput Crusher Control
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Solution Overview
Problem
Existing aggregate production systems face challenges in achieving consistent output due to variability in input materials and operational conditions, leading to inconsistencies in crushed particle characteristics and increased waste, as real-time data collection and processing are difficult at high throughput rates.
Innovation Solution
A system utilizing high and low resolution sensors to train a characterization model for real-time particle analysis, enabling prediction of post-crushing characteristics and adjusting crushing system parameters to align with target outputs, integrated with automated feedback control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If comprehensive testing of output aggregate is performed to ensure consistent particle characteristics, then manufacturing precision is improved, but productivity decreases due to the time-consuming nature of traditional testing methods
Solution Approach 1:
The patent replaces traditional mechanical testing methods with optical sensing systems (cameras, lasers) and automated image processing to characterize aggregate particles. This substitution enables rapid, non-contact measurement of particle properties, maintaining manufacturing precision while dramatically increasing productivity by eliminating the time-consuming nature of manual testing.
2Adaptability or versatility
If real-time data collection on rock crusher output is implemented, then adaptability is improved, but device complexity increases due to the need for high-speed sensing and processing systems
Solution Approach 1:
The patent introduces a characterization model as an intermediary between the optical sensing system and the control system. This model processes sensor data to extract meaningful particle characteristics and predicts crusher output, simplifying the overall system architecture while enabling real-time adaptability. The model acts as a mediator that translates complex sensor data into actionable insights without requiring direct complex processing throughout the entire system.
3Measurement precision
If high resolution sensor data is collected for every particle to ensure accurate characterization, then measurement precision is improved, but loss of time increases due to the extensive data processing required
Solution Approach 1:
The patent applies local quality by using high-resolution sensing only for critical particle characteristics that most significantly impact product quality, while using lower-resolution sensing for less critical parameters. The characterization model selectively processes data based on particle type and crusher settings, focusing computational resources on the most important measurements. This approach maintains measurement precision for key parameters while reducing overall data processing time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterization of aggregate particles. A method includes obtaining, from a set of low fidelity sensors, first sensor data of a first portion of particles; obtaining, from a set of high fidelity sensors, second sensor data of the first portion of particles, the second sensor data comprising a higher fidelity representation of characteristics of the first portion of particles than the first sensor data; training a characterization model using the first sensor data and the second sensor data, the training comprising: providing, as training data to the characterization model, the second sensor data; and processing the second sensor data with the characterization model to correlate the first sensor data with the second sensor data. The first sensor data can indicate shape characteristics of each particle; and the second sensor data indicates a surface area of each particle.