Achieved 70% Reduction in downtime of conveyor failures with Ripik AI conveyor belt monitoring system. This ensured smoother operations, minimized downtime, and delivered significant cost savings.
Manual inspections frequently overlooked early signs of refractory damage, resulting in unplanned downtime. By contrast, AI-driven monitoring enables timely detection of wear, cutting downtime by 50% and enhancing operational reliability.
Reducing inconsistencies in calorific values through AFR monitoring optimizes combustion efficiency, minimizes equipment damage, and lowers energy consumption.
Achieved an 82% reduction in crusher blockages and a 20% decrease in unwanted stoppages in cement crusher operations by detecting oversized rocks in real time. This led to smoother operations, reduced downtime, and substantial cost savings.
Real-time camera intelligence to monitor materials, equipment, and processes, driving efficiency and consistency in industrial operations.
Hosts and manages hundreds of Vision AI Agents, enabling you to customize, deploy, and scale real-time monitoring solutions instantly across operations.
Let us walk you through a tailored demo experience.
Achieved 70% Reduction in downtime of conveyor failures with Ripik AI conveyor belt monitoring system. This ensured smoother operations, minimized downtime, and delivered significant cost savings.
Manual inspections frequently overlooked early signs of refractory damage, resulting in unplanned downtime. By contrast, AI-driven monitoring enables timely detection of wear, cutting downtime by 50% and enhancing operational reliability.
Reducing inconsistencies in calorific values through AFR monitoring optimizes combustion efficiency, minimizes equipment damage, and lowers energy consumption.
Achieved an 82% reduction in crusher blockages and a 20% decrease in unwanted stoppages in cement crusher operations by detecting oversized rocks in real time. This led to smoother operations, reduced downtime, and substantial cost savings.
Real-time camera intelligence to monitor materials, equipment, and processes, driving efficiency and consistency in industrial operations.
Hosts and manages hundreds of Vision AI Agents, enabling you to customize, deploy, and scale real-time monitoring solutions instantly across operations.
Let us walk you through a tailored demo experience.
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.
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.
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
Achieved 70% Reduction in downtime of conveyor failures with Ripik AI conveyor belt monitoring system....
Manual inspections frequently overlooked early signs of refractory damage, resulting in unplanned downtime....
Reducing inconsistencies in calorific values through AFR monitoring optimizes combustion efficiency,...
Achieved an 82% reduction in crusher blockages and a 20% decrease in unwanted stoppages in cement crusher...
The Flare Stack Monitoring System delivered over 90% measurement accuracy, replacing manual judgment...
Ripik AI's Ladle Activity Tracker enhanced ladle operations in steel plants with comprehensive real-time...
Ripik AI's material sizing solution achieved 95% accuracy, significantly enhancing raw material monitoring...
Ripik AI's Volumetric Estimation using LIDAR has transformed stockpile management, achieving over 95%...
In the world of cost-competitive steel market, our client managed to improve fuel rate and throughput...
See how Ripik.AI's real-time asset monitoring solution transformed a leading EPC company's operation...
