Zero Downtime, High Yield, AI-Driven Vision
Zero Downtime, High Yield, AI-Driven Vision
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.
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.
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.
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.
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.
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.
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 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.
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 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 enables real-time identification of irregularities in material composition, allowing for immediate corrective actions to maintain optimal operational efficiency and reduce risks.
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.
See how leading companies across steel, cement, oil & gas, energy, automotive, chemicals, pharmaceuticals, FMCG, and other industries are transforming their operations with Ripik AI’s Vision AI solutions—driving real-time insights, enhanced safety, and intelligent process optimizations across the industrial landscape
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