Automated real time material tracking

Accurate Material sizing, delivered 95% accuracy, boosting sampling frequency by 500x

Ripik AI's computer vision-based raw material sizing enables real-time raw material monitoring for process optimization, enhancing accuracy and efficiency in material handling and reducing operational disruptions.

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A leading steel and mining company focused on process optimization

Client Overview

The client is the world’s largest steel and mining company, with operations in over 60 countries. Producing more than 70 million metric tons of steel annually, the company is a key player in the global market. Their extensive operations leverage cutting-edge technology and a focus on sustainability, enabling them to maintain leadership and drive innovation across the industry.

Problem Statement

Furnace/Boiler operations were frequently disrupted by undetected raw materials quality issues

The client faced frequent disruptions in furnace and boiler operations due to undetected raw material quality deviations. The lack of robust raw material analysis and real-time material tracking systems resulted in undetected inconsistencies in material composition, leading to unplanned downtimes, reduced operational efficiency, and increased maintenance costs. The inability to monitor and address these quality variations in real-time compromised the stability, performance, and reliability of their processes.

Inconsistent Raw Material Size

Size variations in materials like coke, coal, and sinter on conveyor belts often lead to overall process instability, including increased wastage and downtime.​

Inconsistent raw material size, including fines, oversized particles, and foreign objects, impacts permeability, reduces efficiency, increases fly and bed ash, damages conveyors, and harms furnace health.

Time Delay in Sampling

Manual sampling took up to 8 hours, delaying timely adjustments and corrective actions.

Manual sampling required up to 8 hours for data collection and analysis, delaying crucial adjustments and corrective actions. This lag increased the risk of process deviations and inefficiencies.

Poor Accuracy and Irregular Sampling

Traditional sieve sampling provided low accuracy and did not accurately represent the full scope of material fed into the furnace.

Traditional manual sieve sampling provided low accuracy and infrequent measurements. The limited sampling size and frequency failed to capture the full scope of material flow into the furnace, resulting in incomplete data for effective process control.

High Moisture Content

Increased coal moisture lead to In-apt combustion

High moisture levels in raw materials lead to increased fuel consumption, accelerated equipment wear, operational inefficiencies, and higher waste generation, negatively impacting overall performance and costs.

Solution​

Ripik AI leverages real-time granulometry for raw materials using a computer vision system.

  • Real-Time Particle Sizing Ripik Vision’s AI solution enabled real-time detection of size distribution on conveyor belts, achieving over 90% accuracy. This continuous monitoring of material composition ensured optimal flow, minimized disruptions, and improved efficiency, reducing the risk of furnace and boiler issues due to inconsistent raw material quality.
  • Specialized Hardware Specialized cameras with self-cleaning mechanisms were implemented to ensure effective operation in harsh environments. These cameras identified anomalies, such as oversized particles, and enabled immediate corrective actions to maintain operational efficiency.
  • Integrated Analytics Real-time insights into particle size analysis and composition enhanced process control, improving operational stability and reducing material inconsistencies, ultimately boosting efficiency and reliability.
  • Instant real-time Alerts Instant real-time alerts provided to operators with notifications for any deviations in material composition or process parameters, enabling prompt corrective actions and ensuring smooth operations.
  • Automated Anomaly Detection Automated Anomaly Detection enables real-time identification of irregularities in material composition, allowing for immediate corrective actions to maintain optimal operational efficiency and reduce risks.
raw material monitoring

Results and Impact

our Material sizing platform delivered 95% accuracy, boosting sampling frequency by 500x

The material sizing solution achieved 95% accuracy, enhancing raw material monitoring and boosting sampling frequency by 500x. This eliminated the 8-hour lag from sampling to reporting, transitioning to real-time reporting and alerts. Rather than analyzing just a sample, over 90% of the incoming material was assessed, enabling more precise monitoring and faster corrective actions.

$1.2M

Expected value generation annually

The solution led to $1.2M in annual value generation, delivering substantial cost savings and enhancing operational efficiency through advanced raw material monitoring and optimized process management.

4.5kT

Volume
improvement

The solution resulted in a 4.5kT volume improvement, enhancing material efficiency and boosting overall production capacity by optimizing raw material handling and process control.

0.7%

Fuel rate
reduction

The solution resulted in a 0.7% reduction in fuel rate, a substantial cost saving, optimizing energy consumption and lowering operational costs through enhanced raw material monitoring and process control.