Achieved 82% fewer crusher blockages and 20% lower unwanted stoppages in cement crusher operations by detecting oversized rocks in real time. This ensured smoother operations, minimized downtime, and delivered significant cost savings.
Client Overview
Our client is a leading building materials supplier, producing aggregates, cement, and concrete across North America. They have a strong commitment to sustainability, with a focus on recycling materials, efficient water management, and reducing emissions to minimize environmental impact. With a solid financial foundation, they generate over $5 billion in annual revenue, reinforcing their position as a key player in the construction materials industry.
The client faced significant challenges due to inadequate material monitoring systems, leading to equipment damage, blockages, and efficiency losses from oversized rocks. Frequent downtime, higher maintenance costs, and increased fuel consumption further impacted operations. Reliance on human supervision introduced bias, while manual sieve sampling provided low accuracy and infrequent measurements, making real-time monitoring unreliable.
Large rocks in the apron feeder damage chains, rollers, and pans, causing structural failures. The excessive impact strains the drive system, reducing equipment lifespan. This leads to increased maintenance costs and unplanned downtime.
Large rocks in the apron feeder cause blockages, disrupting material flow and straining crushers. This leads to increased wear, operational inefficiencies, and unplanned downtime.
Oversized rocks on the conveyor before the primary crusher reduce efficiency by causing blockages and uneven material flow. This leads to increased wear, higher energy consumption, and frequent downtime.
Oversized particles after the secondary crusher reduce raw material uniformity, decreasing grinding efficiency in the raw mill. This increases energy demand and fuel consumption, impacting overall process optimization and operational costs.
Our Vision AI platform enhanced material handling by leveraging real-time computer vision algorithms to detect oversized rocks, triggering automated alerts for timely removal and preventing equipment damage. Advanced size optimization improved crusher efficiency, reducing wear on critical components and minimizing unplanned downtime. Additionally, precise limestone sizing led to optimized combustion in kilns, lowering fuel consumption and enhancing energy efficiency, ultimately driving cost savings and process stability.
Our Vision AI platform is expected to generate $440K in annual value by preventing crusher damages, reducing downtime, and optimizing fuel consumption. Automated rock detection minimizes unplanned shutdowns and maintenance costs, enhancing crusher efficiency and drive cost savings and operational stability.
Our Vision AI platform achieved an 82% reduction in crusher blockage by detecting and removing oversized rocks in real time. This optimization minimized downtime, improved throughput, and reduced wear on critical crusher components, enhancing overall operational efficiency and cost savings.
Our Vision AI platform reduced unwanted stoppages by 20% in cement crusher operations through real-time detection and removal of oversized rocks. This improvement enhanced material flow, minimized downtime, and optimized equipment utilization, leading to greater efficiency and cost savings.