In the heart of China’s capital, researchers at the China University of Mining and Technology-Beijing are revolutionizing the way we monitor material flow in belt conveyors, a critical component in the energy and mining sectors. Led by LIU Yi from the School of Artificial Intelligence, a team has developed a novel laser-based detection method that promises to enhance accuracy and reliability in material flow measurement, potentially saving industries millions in operational costs.
The challenge at hand is a familiar one for those in the industry: existing LiDAR-based systems often struggle with abnormal point cloud data, leading to inaccuracies in describing the surface state of materials on conveyor belts. “This inaccuracy can result in significant losses, both in terms of material waste and operational inefficiencies,” explains LIU Yi. To tackle this issue, the team turned to an unconventional ally: Akima interpolation.
Akima interpolation, a method known for its smooth and shape-preserving properties, was employed to obtain the cross-sectional area of material on the belt. By combining this with the conveyor’s operating speed and LiDAR scanning frequency, the team could calculate the material volume within a single scan cycle. The results were impressive. Simulation results showed that denoising outlier points from the LiDAR output point cloud could effectively identify and correct abnormal data, bringing calculated values closer to the actual material cross-sectional area.
The team conducted comparative experiments using both the traditional sector-triangle calculation method and their new Akima interpolation method. The results were stark. The sector-triangle method showed lower and less stable accuracy, while the Akima interpolation method consistently achieved accuracy above 90%. “This high reliability enables accurate measurement of both instantaneous and total material flow,” says LIU Yi, highlighting the potential impact on industries that rely heavily on belt conveyors.
The implications for the energy sector are substantial. Accurate material flow detection can lead to better resource management, reduced waste, and improved operational efficiency. As the world grapples with climate change and the need for sustainable energy solutions, such advancements become even more critical. The research, published in ‘Gong-kuang zidonghua’ (translated to ‘Mining and Automation’), opens new avenues for exploration in the field of material flow detection.
Looking ahead, this research could shape future developments in the field, inspiring further innovations in laser-based detection methods. As industries strive for greater precision and efficiency, the work of LIU Yi and his team serves as a beacon, illuminating the path forward. In a world where every ounce of resource counts, their method could be the key to unlocking a more sustainable and efficient future for the energy sector.