Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Biological Treatment of Effluent and Waste Water01:30

Biological Treatment of Effluent and Waste Water

Biological wastewater treatment relies on the metabolic activity of microorganisms to remove pollutants from sewage. In modern treatment systems, this process is organized into sequential stages that progressively reduce solid material, dissolved organic matter, and microbial contamination. Each stage plays a distinct role in improving water quality and preparing the effluent for safe discharge or reuse.Primary and Secondary TreatmentPrimary treatment is a physical process that removes large...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep transfer learning compared to subject-specific models for sEMG decoders.

Journal of neural engineering·2022
Same author

Hand Gesture Recognition in Automotive Human⁻Machine Interaction Using Depth Cameras.

Sensors (Basel, Switzerland)·2018
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation
11:31

Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation

Published on: July 13, 2012

33.8K

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies.

Tom Maus1, Nico Zengeler1, Dorothee Sänger2

  • 1Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary

Ultrasonic sensors in waste sorting facilities show potential for cost-effective automation but face accuracy challenges due to waste variability and environmental factors. Improving data quality and machine learning models is key for accurate waste volume determination and sustainable management.

Keywords:
machine learningultrasonic sensorsvolume determinationwaste sorting facilities

More Related Videos

Author Spotlight: Advancing Anaerobic Microbiota Research Using a Novel Respirometry Protocol
06:11

Author Spotlight: Advancing Anaerobic Microbiota Research Using a Novel Respirometry Protocol

Published on: April 26, 2024

1.2K
Setting a Successful Sorting for Extracellular Vesicle Isolation
08:37

Setting a Successful Sorting for Extracellular Vesicle Isolation

Published on: October 11, 2024

997

Related Experiment Videos

Last Updated: Jun 27, 2026

Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation
11:31

Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation

Published on: July 13, 2012

33.8K
Author Spotlight: Advancing Anaerobic Microbiota Research Using a Novel Respirometry Protocol
06:11

Author Spotlight: Advancing Anaerobic Microbiota Research Using a Novel Respirometry Protocol

Published on: April 26, 2024

1.2K
Setting a Successful Sorting for Extracellular Vesicle Isolation
08:37

Setting a Successful Sorting for Extracellular Vesicle Isolation

Published on: October 11, 2024

997

Area of Science:

  • Environmental Science
  • Engineering
  • Computer Science

Background:

  • Ultrasonic sensors offer a cost-effective solution for automating volume determination in waste sorting facilities.
  • Challenges include irregular waste shapes, varied compositions, and environmental factors affecting sensor accuracy.
  • Inconsistencies between bunker and conveyor belt measurements highlight the need for standardized bale production.

Purpose of the Study:

  • To evaluate the effectiveness of ultrasonic sensors for waste volume determination.
  • To identify and address waste-material-specific challenges impacting measurement accuracy.
  • To explore strategies for enhancing prediction reliability in machine learning applications for waste management.

Main Methods:

  • Case study analysis of three European waste sorting facilities.
  • Dataset analysis focusing on sensor usability, data quality, and material specifics.
  • Exploration of related research in waste volume determination and machine learning.

Main Results:

  • Ultrasonic sensor accuracy is significantly impacted by waste material properties and environmental conditions.
  • Limited datasets, undocumented machine modifications, and sensor failures constrain machine learning prediction reliability.
  • Discrepancies in volume measurements indicate a need for improved standardization in bale production.

Conclusions:

  • Standardizing bale production and improving data quality are crucial for accurate waste volume measurement.
  • Further research is needed to overcome machine learning limitations in this domain.
  • Enhanced waste volume accuracy can significantly advance sustainable waste management practices.