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.
Reduce inconsistent calorific values, oversized particles, foreign objects, and high moisture levels with AI-driven solutions for optimized alternative fuels in cement plants.
The shift towards alternative fuels (afr) in the cement industry offers significant advantages, including reduced carbon emissions, lower energy costs, and a more sustainable production process. However, managing afr presents several challenges that can impact operational efficiency.
AFR consists of various fuels like biomass, paper, tyre and plastics. The feed with varying materials can't be tracked 24/7, with zero to no visibility, unexpected elements get introduced into the process, potentially affecting clinker chemistry and cement quality.​
Plant teams rely on lab results to adjust the AFR mix, but these results are not real-time, causing inefficiencies in combustion and energy output. The time lag between sampling and receiving analysis results can be several hours or even days.​
Using AFR often leads to significant variations in calorific value because of the diverse types of materials involved. These variations can cause temperature fluctuations in the kiln, affecting clinker quality and production stability.
Large particles, such as stones or other foreign particles, can disrupt the smooth operation of AFR processing. These oversized materials can cause blockages in the fuel feeding system, leading to interrupted fuel supply and kiln instability.​
Real-time monitoring of AFR feed belts: Performs real-time monitoring of AFR feed belts to detect and classify alternative fuels such as plastics, rubber, and biomass used in cement kilns.
Calorific value estimation: Uses AI models to predict and adjust the AFR mix calorific value dynamically for consistent combustion efficiency.
Calorific value prediction: Continuously calculates calorific value in real time based on AFR composition, particle size, and moisture content.
Adaptive combustion control: Automatically fine-tunes airflow and fuel input to maintain stable kiln temperature and optimize energy utilization.
Real-Time monitoring: Continuously monitors AFR belts to identify oversized particles, metal fragments, and foreign materials before they cause blockages or equipment damage..
Instant alerts: Automated notifications inform operators of potential blockages or damage risks.
Real-Time moisture sensing: AI algorithms analyze moisture levels from live camera feeds and environmental data.
Historical moisture trends: Generates detailed reports of historical moisture variations to identify recurring inefficiencies and improve fuel preparation.
Instant alerts and recommendations: Operators receive actionable alerts when AI identifies anomalies like inconsistent feed rates, irregular combustion patterns, and thermal deviations, preventing critical system failures.
Historical video feed : Analyze historical video feeds for postmortem analytics to identify critical events, behavior, and operational patterns.
Comprehensive reporting: Detailed reports highlight recurring issues and trends, enabling proactive steps for continuous improvement
Implementing Vision AI for alternative fuel resources in the cement industry ensures optimal combustion efficiency, minimizes equipment damage, and reduces energy consumption, ultimately reducing the carbon footprint in the cement industry.
Real-time data optimizes the alternative fuel and resources mix for complete combustion, reducing energy waste.
Optimized combustion and minimized downtime lower fuel expenses and boost operational efficiency.
Replacing traditional method with alternative fuel is reducing carbon footprint in cement manufacturing.
Deployment is fast and flexible. Most AI-based AFR monitoring systems can be integrated using existing cameras and PLC infrastructure. Setup and calibration take just a few days—not months—so plants can begin optimizing fuel performance and seeing measurable results almost immediately.
Vision AI agents works better than regular sensors for AFR monitoring because it’s smart, reliable, and easy to maintain. While sensors often fail due to heat, dust, or vibration, Vision AI keeps watching the process all the time and spots problems like uneven fuel feed or blockages right away. It helps plants run more smoothly, use fuel efficiently, and avoid downtime — all without constant calibration or manual checks.
Yes. Vision AI systems are designed to integrate seamlessly with existing DCS, PLC, or SCADA infrastructure. They can share real-time data, alerts, and insights directly with control systems, supporting smarter and more automated fuel management.
Cement plants typically see a 2–5% increase in TSR, 3–7% higher kiln uptime, and significant fuel cost savings with AFR monitoring automation. Most achieve full ROI within 6–12 months, driven by improved fuel efficiency, fewer stoppages, and more stable kiln operation.
A powerful suite of intelligent agents working in sync to transform manufacturing with speed, precision, and autonomy.
