Transform Your Idea

Particle size distribution recognition system

Expertise:
Backend
Frontend
DevOps
Tech Stack:
MySQL / PostgreSQL
Docker
OpenCV
Client: Industrial enterprise
Our client is a large company involved in the production and processing of materials based on sand and gravel. Their core business processes include the use of conveyor belts for transporting and processing materials in various industrial facilities. The company has several critical production stages where maintaining high-quality standards of materials and ensuring uninterrupted equipment operation are essential.
About the Client
The goal of the project was to develop and implement a system for monitoring the granulometric composition of materials transported via conveyor belts. The system was designed not only to improve product quality control but also to minimize the risks of equipment breakdowns related to deviations in material granulometry.
Project Overview

Challenges & Objectives

  • Monitoring the granulometric composition of materials in real time.
  • Preventing emergency situations related to equipment breakdowns.
  • Extending equipment lifespan by reducing wear and stress.
  • Optimizing the operation of crushing units and conveyor lines for better efficiency.
  • Ensuring stable and high-quality performance throughout all stages of the production process.
Implementation & Tech Stack
To address the challenge, a granulometric composition recognition system was developed. The system utilizes computer vision and machine learning techniques to analyze and classify the granulometric characteristics of materials passing through conveyor belts. The system is integrated with sensors installed on the conveyor lines and can transmit real-time data on material composition.
Tech stack:
01
Computer vision for analyzing material images.
02
Machine learning for training models on historical data and predicting material composition.
03
API interfaces for integration with existing monitoring and control systems.
04
Cloud technologies for data processing and remote monitoring.

Outcomes & Business Impact

  • Prevention of emergency situations: The system enables early detection of anomalies in material composition, reducing the likelihood of equipment breakdowns.
  • Increased equipment lifespan: By optimizing crusher operating modes and preventing overloads, the equipment operates longer and more efficiently.
  • Optimization of crusher performance: The system automatically adjusts the crusher’s operating parameters based on material composition, increasing both productivity and the quality of processing.
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