Maus, T.; Zengeler, N.; Sänger, D.; Glasmachers, T. Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies. Sensors2024, 24, 2114.
Maus, T.; Zengeler, N.; Sänger, D.; Glasmachers, T. Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies. Sensors 2024, 24, 2114.
Maus, T.; Zengeler, N.; Sänger, D.; Glasmachers, T. Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies. Sensors2024, 24, 2114.
Maus, T.; Zengeler, N.; Sänger, D.; Glasmachers, T. Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies. Sensors 2024, 24, 2114.
Abstract
In this study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited data sets, undocumented modifications to machine-settings and sensor failures, this task renders a challenging application area for Machine Learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues as sensor usability, data quality and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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