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

Name: Raw Material Sizing
Brand: Ripik AI
Availability: InStock

Ripik AI's Volumetric Estimation using LIDAR has transformed stockpile management, achieving over 95% measurement accuracy, eliminating 100% of safety hazards, and saving over 40 man-days annually, driving efficiency, safety, and precision in operations.

Trusted by leading global enterprises

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 on conveyor belts often lead to overall process instability.

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.

Conveyor
Sway

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 Stockpile Monitoring Using LIDAR

Conveyor monitoring uses specialized hardware and multi-camera systems designed to endure demanding belt operation conditions, ensuring reliable performance while continuously monitoring the belt surface for wear, misalignment, and anomalies.

3D map of the material

Real-time conveyor belt monitoring enables continuous tracking of belt conditions, instantly detecting wear, misalignment, and material spillage. Early identification of conveyor health issues allows for timely repairs, preventing further damage and minimizing unplanned downtime.

PLC Control on Anomalies

Programmable Logic Controllers (PLCs) integrate with computer vision systems to enable real-time anomaly detection and trigger immediate corrective actions, such as stopping the conveyor belt to prevent damage.

Automated Anomaly Detection & Instant alerts

AI detects anomalies on the conveyor belt and sends instant alert notifications via dashboards, WhatsApp, or emails, ensuring rapid response to potential issues.

Historical video feeds & insightful report

The system archives of past conveyor belt footage conditions, allowing operators to review past events and identify patterns over time. Detailed reports provide actionable insights that support data-driven maintenance decisions and improve overall operational efficiency.

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 annual value generation

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

Potential foreign object failures prevented in first 3 months

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%

Reduction in downtime due to conveyor failures

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.

OUR success stories

Our role in driving
customer success

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