Self-Sorting of Solid Waste using Machine Learning

Paper


Chan, Tyson, Cai, Jacky H., Chen, Francis and Chan, Ka C.. 2020. "Self-Sorting of Solid Waste using Machine Learning." Hernes, Marcin, Wojtkiewicz, Krystian and Szczerbicki, Edward (ed.) 12th International Conference on Computational Collective Intelligence (ICCCI 2020). Da Nang, Vietnam 30 Nov - 03 Dec 2020 Cham, Switzerland. https://doi.org/10.1007/978-3-030-63119-2
Paper/Presentation Title

Self-Sorting of Solid Waste using Machine Learning

Presentation TypePaper
AuthorsChan, Tyson (Author), Cai, Jacky H. (Author), Chen, Francis (Author) and Chan, Ka C. (Author)
EditorsHernes, Marcin, Wojtkiewicz, Krystian and Szczerbicki, Edward
Journal or Proceedings TitleAdvances in Computational Collective Intelligence: 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings
ERA Conference ID43259
Number of Pages12
Year2020
Place of PublicationCham, Switzerland
ISBN9783030631185
9783030631192
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-63119-2
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-030-63119-2_5
Conference/Event12th International Conference on Computational Collective Intelligence (ICCCI 2020)
International Conference on Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems
Event Details
International Conference on Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems
ICCCI
Rank
C
C
C
C
C
C
Event Details
12th International Conference on Computational Collective Intelligence (ICCCI 2020)
Event Date
30 Nov 2020 to end of 03 Dec 2020
Event Location
Da Nang, Vietnam
Abstract

In waste recycling, the source separation model, decentralises the sorting responsibility to the consumer when they dispose, resulting in lower cross contamination, significantly increased recycling yield, and superior recovery material quality. This recycling model is problematic however, as it is prone to human error and community-level participation is difficult to incentivise with the greater inconvenience being placed on consumers. This paper aims to conceptualise a solution by proposing a unique mechatronic system in the form of a self-sorting smart bin. It is hypothesised that in order to overcome the high variability innate to disposed waste, a robust supervised machine learning classification model supported by IoT integration needs to be utilised. A dataset comprising of 680 samples of plastic, metal and glass recyclables was manually collected from a custom-built identification chamber equipped with a suite of sensors. The dataset was then split and used to train a modular neural network comprising of three concurrent individual classifiers for images (CNN), sounds (MLP) and time series (KNN-DTW). The output class probabilities were then integrated by one combined classifier (MLP), resulting in a prediction time of 0.67 s per sample, a prediction accuracy of 100%, and an average confidence of 99.75% averaged over 10 runs of an 18% validation split.

KeywordsWaste automation; Recycling; Neural network
ANZSRC Field of Research 2020400702. Automation engineering
400708. Mechatronics hardware design and architecture
460205. Intelligent robotics
461104. Neural networks
Byline AffiliationsUniversity of New South Wales
University of Southern Queensland
SeriesCommunications in Computer and Information Science
Institution of OriginUniversity of Southern Queensland
Book TitleAdvances in Computational Collective Intelligence (Vol. 1287)
Chapter Number5
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