Self-Sorting of Solid Waste using Machine Learning

Chan, Tyson and Cai, Jacky H. and Chen, Francis and Chan, Ka C. ORCID: https://orcid.org/0000-0002-8756-2991 (2020) Self-Sorting of Solid Waste using Machine Learning. In: 12th International Conference on Computational Collective Intelligence (ICCCI 2020), 30 Nov - 3 Dec 2020, 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Date Deposited: 27 Nov 2020 05:38
Last Modified: 04 Jan 2021 01:01
Uncontrolled Keywords: Waste automation; Recycling; Neural network
Fields of Research (2008): 09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080101 Adaptive Agents and Intelligent Robotics
Fields of Research (2020): 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400702 Automation engineering
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400708 Mechatronics hardware design and architecture
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460205 Intelligent robotics
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks
Socio-Economic Objectives (2008): B Economic Development > 89 Information and Communication Services > 8998 Environmentally Sustainable Information and Communication Services > 899802 Management of Solid Waste from Information and Communication Services
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1899 Other environmental management > 189999 Other environmental management not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/978-3-030-63119-2
URI: http://eprints.usq.edu.au/id/eprint/40171

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