Characterising the composition of waste-derived fuels using a novel image analysis tool

Area of Science:

• Waste Management and Valorization
• Image Analysis and Remote Sensing
• Materials Science

Background:

• Accurate characterization of waste-derived fuels is crucial for efficient energy recovery.
• Traditional methods for waste composition analysis can be time-consuming and labor-intensive.
• Image analysis offers a potential non-destructive and rapid alternative for material assessment.

Purpose of the Study:

• To apply and evaluate an innovative image analysis approach for determining the composition of shredded waste materials.
• To assess the accuracy of the image analysis method by comparing determined composition with known sample compositions.
• To identify potential improvements for the image-based technique in future applications.

Main Methods:

• Waste materials were collected, shredded (<150 mm), and formed into representative samples.
• Digital images of samples were captured using 10x10 cm and 20x20 cm quadrats on a conveyor belt setup.
• ERDAS Imagine software processed images to determine component area coverage, converted to mass using density data.

Main Results:

• A strong correlation (mean r=0.89) was observed between image analysis-derived composition and known sample compositions.
• Sample area coverage and particle size influenced the accuracy of component monitoring.
• The study presents initial findings on an adapted image-based method for waste material analysis.

Conclusions:

• The developed image analysis method demonstrates significant potential for accurate waste-derived fuel composition determination.
• Further refinement of the image analysis technique, particularly concerning sample area and particle size, can enhance accuracy.
• This approach offers a promising tool for advancing waste characterization and supporting the circular economy.