PRE2019 3 Group5: Difference between revisions
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===Enterprise=== | ===Enterprise=== | ||
Recycling firms are struggling to be profitable in large part due to expensive and inefficient separation of waste feed-streams. An automated bin will judge much more accurately which products are likely to be recyclable than the average consumer. This improved recycling prior to waste collection would drastically lower the cost of recycling and increase profitability of these plants, therefore encouraging new investments into such businesses. The increase of separated waste arriving at recycling plants rather than landfills will also allow for these plants to invoke the economics of scaling up to further increase profitability, making such businesses even more attractive. | Recycling firms are struggling to be profitable in large part due to expensive and inefficient separation of waste feed-streams. An automated bin will judge much more accurately which products are likely to be recyclable than the average consumer. This improved recycling prior to waste collection would drastically lower the cost of recycling and increase profitability of these plants, therefore encouraging new investments into such businesses. The increase of separated waste arriving at recycling plants rather than landfills will also allow for these plants to invoke the economics of scaling up to further increase profitability, making such businesses even more attractive. It thus likely that widely accessible smart bins will grow the recycling industry. | ||
==Approach, milestones and deliverables== | ==Approach, milestones and deliverables== | ||
Revision as of 00:22, 9 February 2020
Group members
| Name | Student ID | Department |
|---|---|---|
| Ana Maria Risnoveanu | 0000000 | Electrical Engineering |
| Stacey Elshove | 1279998 | Psychology and Technology |
| Petru Radulescu | 1371320 | Psychology and Technology |
| Yiqin Hou | 1281135 | Electrical Engineering |
| Tobias Hilpert | 1281070 | Chemical Engineering |
Problem Statement
Today the technology for recycling already exists and is put to use in most first world countries, but at times it can be inefficient because of mistakes made by all of us when throwing the trash, either unwillingly or because of our ignorance. So to mitigate this and help create a better planet for the future generation, we have come up with the idea of a smart trash can, that will use a huge array of sensors and cameras to determine whether the waste should go in the glass, plastic or paper bin. This product will be small enough to fit in our homes and, in the future, it could be upscaled to service a full residence, like a block of flats.
Objectives
- Design an affordable smart trashcan that fits into our homes.
- Use machine learning and AI to sort the trash into their corresponding compartment.
- Find suitable technologies to achieve this.
USE Aspects
User
Society
Enterprise
Recycling firms are struggling to be profitable in large part due to expensive and inefficient separation of waste feed-streams. An automated bin will judge much more accurately which products are likely to be recyclable than the average consumer. This improved recycling prior to waste collection would drastically lower the cost of recycling and increase profitability of these plants, therefore encouraging new investments into such businesses. The increase of separated waste arriving at recycling plants rather than landfills will also allow for these plants to invoke the economics of scaling up to further increase profitability, making such businesses even more attractive. It thus likely that widely accessible smart bins will grow the recycling industry.
Approach, milestones and deliverables
Task distribution
Week1
| Name[total hours of work] | Tasks and hours |
|---|---|
| Ana Maria Risnoveanu | ... |
| Stacey Elshove | ... |
| Petru Radulescu | Objectives; Problem statement; ... |
| Yiqin Hou[?h] | Papers about X-ray imaging[1][2][3][4]/Object recognition[5] searched and studied[?h]; summary[?h]; Learn wikitext and make template for the group wiki[4h] |
| Tobias Hilpert[14h] | Literature study into waste sorting[4h], Research into Infrared spectroscopy[8h], Learned wikitext[2h], Wrote Enterprise and Society aspect[] |
SotA
Detection
X-ray
Inspired by security machines, X-ray imaging could be a usefull tool in detcting the type of rubbish. X-ray imaging [3]makes use of the property of X-ray that it attenuates differently accorss difderent materials. For example metal atoms and ions attenuate more X-ray than normal organic tissue, such as fat and protein. Some new X-ray imaging techniques could even determine the chemical structures that form within the materials[4]. With such techiniques and some morden X-ray imaging detectors [2], the bin can distinguish materials much more accurate than using normal X-ray imaging. It is also possible to distinguish different types of plastics with X-ray imaging[1], which further increases the recycling sorting process.
Despite its reliable performance in detecting metal and different kinds of organic materials, an X-ray imaging system is too expensive to implement in a household trash bin. Even the cheapest X-ray tubes cost $100 to $500 each, let alone the detectors, power supply and other systems.
Computer vision and image processing
Object recognition [5]
Sorting systems
Infrared Spectroscopy
Infrared spectroscopy can be classified into different categories based on the wavelengths used. This detection method is both cheap and fast enough for practical applications in waste sorting. Near Infrared (NIR, wavelength 1-1.7µm) Spectroscopy is already deployed for uses in plastic separation. NIR has the ability to excite overtones of molecular vibrations and therefore can give information on the chemical composition of the sample. [6] NIR spectroscopy can also be used for different kinds of waste, such as construction debris [7]. There are clear limitations when analysing black and coloured samples though, as their reflectivity in the NIR spectrum is too low to allow for signal-to-noise ratios high enough for proper identification. To overcome this difficulty NIR spectroscopy can be enhanced with longer wavelengths. It has been shown that combining NIR with Midwave Infrared (MWIR, wavelength 3-12µm) significantly improves the performance of detectors for black and opaque products. [8] This can be extended to multiple spectra in hyperspectral imaging techniques, which probe a wide range of wavelengths even in the visible spectrum for material identification. These systems have also been shown to work at the speeds which would be needed for practical applications. [9] FTIR spectrometers are simple enough devices that handheld versions with sensitivities good enough to separate materials exist. [10].
References
- ↑ 1.0 1.1 Peco-InspX. Detecting Plastics with X-Ray Inspection Systems. Date accessed: 2020-02-05. https://www.peco-inspx.com/blog/x-ray-detectable-plastics/
- ↑ 2.0 2.1 Sol M. Gruner.(2012). "X-ray imaging detectors" https://physicstoday.scitation.org/doi/10.1063/PT.3.1819 Pulisher: American Institute of Physics. Date accessed: 2020-02-06
- ↑ 3.0 3.1 Mary E.Coles. (1999). "8. X-Ray Imaging". https://doi.org/10.1016/S0076-695X(08)60419-6 . 35: 301-336. Date accessed: 2020-02-08.
- ↑ 4.0 4.1 Schroer, Christian G. (2011). "X-ray imaging: The chemistry inside". Nature. 476: 159-160. https://www-nature-com.dianus.libr.tue.nl/articles/476159a Date accessed: 2020-02-06
- ↑ 5.0 5.1 Liu, Ying-Ho; Lee, Anthony J.T.; Chang, Fu. (2012). "Object recognition using discriminative parts". Computer Vision and Image Understanding. 116: 854-867.
- ↑ D M Scott 1995 Meas. Sci. Technol. 6 156 ‘A two-colour near-infrared sensor for sorting recycled plastic waste’
- ↑ Iñigo Vegas, Kris Broos, Peter Nielsen, Oliver Lambertz, Amaia Lisbona, Upgrading the quality of mixed recycled aggregates from construction and demolition waste by using near-infrared sorting technology, Construction and Building Materials, Volume 75, 2015, Pages 121-128, ISSN 0950-0618, https://doi.org/10.1016/j.conbuildmat.2014.09.109.
- ↑ Offer Rozenstein, Eldon Puckrin, Jan Adamowski, Development of a new approach based on midwave infrared spectroscopy for post-consumer black plastic waste sorting in the recycling industry, Waste Management, Volume 68, 2017, Pages 38-44, ISSN 0956-053X, https://doi.org/10.1016/j.wasman.2017.07.023.
- ↑ A. C. Karaca, A. Ertürk, M. K. Güllü, M. Elmas and S. Ertürk, "Automatic waste sorting using shortwave infrared hyperspectral imaging system," 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, FL, 2013, pp. 1-4. doi: 10.1109/WHISPERS.2013.8080744
- ↑ Masanori Kumagai, Hideto Suyama, Tomoaki Sato, Toshio Amano, and Nobuaki Ogawa, "Discrimination of Plastics Using a Portable near Infrared Spectrometer," J. Near Infrared Spectrosc. 10, 247-255 (2002)