| EPSRC Reference: |
EP/W026228/1 |
| Title: |
Automatic quality assessment of waste plastic bales through hybrid sensing and data driven modelling |
| Principal Investigator: |
Wang, Dr L |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Engineering & Digital Arts |
| Organisation: |
University of Kent |
| Scheme: |
New Investigator Award |
| Starts: |
01 October 2022 |
Ends: |
30 September 2025 |
Value (£): |
401,037
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Image & Vision Computing |
| Instrumentation Eng. & Dev. |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
Plastic waste is one of the most serious environmental challenges across the world. Currently, around 381 million tonnes of plastic waste are generated every year in the worldwide. However, the current recycling rates are thought to be 14%-18% at the global level. A key issue causing the low recycling rate is the uncertainty of the quality of recycled plastics. It is contaminated with a wide range of non-plastic and non-recycling materials. The current method for inspection of baled materials is core sampling. However, this method is slow to determine results and not accurate if samples extracted are not representative. Therefore, an automatic and non-destructive measurement technology is highly desirable for accurate identification and quantification of materiel type and composition in waste plastic bales and further assessment of the quality of bales.
This project proposes a new measurement methodology for automatic quality assessment of waste plastic bales through hybrid sensing and data driven modelling. The project will start with design and construction of a hybrid sensing unit including a hyperspectral scanner and a 3D capacitive sensor. Meanwhile, considerations about the optimal design of the capacitive sensor with multiple electrodes will be made through finite element modelling. Identification and quantification algorithms based on machine learning and 3D reconstruction techniques will be developed to visualise material distribution and identify material compositions. Quality assessment algorithms based on expert knowledge and artificial intelligence will be developed to classify the plastic waste bale under test into a quality category. At the end of the project, the effectiveness of the proposed measurement methodology will be evaluated through laboratory tests and demonstration trials.
The proposed methodology will provide a new way for inspection of baled materials from both surface and interior of the bale. Meanwhile, this measurement methodology makes the assessment automatic, which will significantly improve the efficiency of recycling processes. In addition, the established assessment criteria will ensure a clearer and more accurate definition of the quality of materials, which will result in more transparent pricing and greater clarity in global trade of waste plastics.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.kent.ac.uk |