Grant: Applied Research and Development Grants - Level 1
Conestoga College Institute of Technology & Advanced Learning
$25,000.00
Development of an artificial neural network for identification of used button cell batteries
EF 2017-18
Résumé
Alkaline batteries in all shapes and sizes are used in many applications and comprise 78% of all batteries soldinto the Canadian market every year. Alkaline (non-rechargeable) and Lithium-Ion (rechargeable) batteriescontain Zinc and rare Earth metals such as Lithium, Cadmium, and Nickel. If these batteries are recycledresponsibly, they will provide new sources for fertilizing agricultural fields, save energy and resources, protectthe environment, and divert waste from landfills.However, considering the volume and variety of waste batteries, automated systems require the ability to sortby chemistry in order to ensure proper recycling. Sorting results in better control over environmental outcomesas separated battery types can be processed by the best specialty recyclers using technology designed forrecycling of specific battery chemistries.In this feasibility study project, we will investigate different ways to employ new technologies for improvingthe efficiency and productivity of household batteries recycling. The core of our study will be to develop areliable method for identifying different types of used button cell batteries using machine vision and ArtificialNeural Networks (ANNs).
Programme
Applied Research and Development Grants - Level 1
Emplacement
Ontario, Canada
Type de bénéficiaire
Other
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
516127-2017
Name nom
Fraser, Andrew
Department département
Head Office
Organization id
22147
Institution établissement
Conestoga College Institute of Technology & Advanced Learning
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2017
Award amount
25000
Program id
CARD1
Program name en
Applied Research and Development Grants - Level 1
Program name fr
Subventions de recherche et développement appliquée - niveau 1
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1561
Committee name en
Applied Research and Development Grants
Committee name fr
Subventions de recherche et développement appliquée
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
1504
Research subject group en
Environmental engineering
Research subject group fr
Génie de l'environnement
Research subject en
Solid waste management (including radioactivity)
Research subject fr
Gestion des déchets dangereux (y compris radioactivité)
Partie
1
Nb partie
1
Application title
Development of an artificial neural network for identification of used button cell batteries
Keyword
NSERC
Application summary
Alkaline batteries in all shapes and sizes are used in many applications and comprise 78% of all batteries soldinto the Canadian market every year. Alkaline (non-rechargeable) and Lithium-Ion (rechargeable) batteriescontain Zinc and rare Earth metals such as Lithium, Cadmium, and Nickel. If these batteries are recycledresponsibly, they will provide new sources for fertilizing agricultural fields, save energy and resources, protectthe environment, and divert waste from landfills.However, considering the volume and variety of waste batteries, automated systems require the ability to sortby chemistry in order to ensure proper recycling. Sorting results in better control over environmental outcomesas separated battery types can be processed by the best specialty recyclers using technology designed forrecycling of specific battery chemistries.In this feasibility study project, we will investigate different ways to employ new technologies for improvingthe efficiency and productivity of household batteries recycling. The core of our study will be to develop areliable method for identifying different types of used button cell batteries using machine vision and ArtificialNeural Networks (ANNs).
