1 / 12

Autonomous Self-Sorting Recycling Bin

Autonomous Self-Sorting Recycling Bin. Focussing on Dense Residential Areas By: Team Jelly Bean Jacky Cai Dr Lydia Hayward Nathan Freitas Andrew Cheng. The Problem. Waste: Australia produces up to 48 million tonnes of waste per year 48% (23 million tonnes ) of this ends up in landfills

Télécharger la présentation

Autonomous Self-Sorting Recycling Bin

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Autonomous Self-Sorting Recycling Bin Focussing on Dense Residential Areas By: Team Jelly Bean Jacky Cai Dr Lydia Hayward Nathan Freitas Andrew Cheng

  2. The Problem • Waste: Australia produces up to 48 million tonnes of waste per year • 48% (23 million tonnes) of this ends up in landfills • Recycling can help solve this issue

  3. Current Residential Recycling System • Single stream recycling • Issues with single stream recycling • Material contamination – recyclables end up in landfills • Increased cost to process contaminated materials Single recycling bin*

  4. Alternative: Source-Separated Recycling • Multiple bins allowing for the separation of recyclables • Solves contamination issue • Current issues with source separation • Inconvenience • Requires more education into proper disposal Source Separated Recycling**

  5. Our Solution • Autonomous self-sorting recycling bin • Similar to current residential system • Recycling has ability to sort into categories of: • Plastic • Glass • Metals • Paper • Technology: Sensor data fusion with supervised machine learning • E.g. image processing, infrared

  6. Target Market • Councils • Councils in dense residential areas • Current focus on apartment buildings with communal waste disposal • Why dense areas • Increased net profit and energy savings in relation to recycling in dense urban areas

  7. Customer Acquisition • Approach a council to implement prototype • Perform case study on benefits of our system compared to existing system • Branch out to other councils • Example: Randwick City Council • Currently sorts waste manually at sorting facility • Transports sorted materials to recycling facilities • Our solution would fit well with Randwick City Council • Our solution can cut out the sorting facility

  8. Revenue Streams • Sale of system • Maintenance of system • Government grants

  9. Current Competitors • Current material recovery facilities • Manual sorting • Human errors • Contamination • Autonomous sorting facilitates (ZenRobotics) • Extremely high costs • Contamination

  10. Value Proposition of our Solution • Reduces inconvenience of source separated model • Reduces contamination risk of single stream model • Meaning: • Increased recycling rates and yield • Improved quality of recovered materials • Increased revenue from recycled material resale • Better for the environment

  11. Why us? • UNSW academia contacts • Research in machine learning and data fusion • Existing work of solution • Diverse mix of expertise • New and fresh idea for waste management

  12. Future Work • Prototype design and manufacture • Approach early adopters and advocates • Don Burke from Burke’s Backyard (Chairman of Australian Environmental Foundation) • Local MP’s – Jenny Leong (Greens, Newtown) • Council contacts - Anthony Collins, manager for sustainability and waste • Approach investors • Team recruitment (sales, marketing, engineers)

More Related