Abstract
Low-value recyclable waste (LVRW) is an essential component of municipal domestic waste. Due to the lightweight and high quantity characteristics, the main disposal methods of LVRW are incineration and landfill, which are not conducive to the environmental protection requirements and the goal of carbon neutrality and emission peak. The paper focuses on the delicate separation of LVRW to realize a cost-effective solution for waste sorting. Firstly, a machine vision (MV) pneumatic sorting equipment was designed based on the MV detection system. Then, a large number of authentic waste images were captured and labeled to train a high accuracy LVRW prediction model through semi-automatic labeling and transfer learning. Subsequently, the LVRW waste sorting solution was designed and constructed, and a comparative experimental study about the MV sorting method and the whole solution was conducted. The experimental results show that the MV sorting method achieves 92% sorting accuracy, and the solution can dispose of 65 tons of LVRW per day with a waste recycling rate of 37.7%. The sorting equipment and solutions developed in the paper can effectively improve the resource utilization of LVRW at a lower cost.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- ABS:
-
Acrylonitrile butadiene styrene
- EPS:
-
Expanded polystyrene
- HDPE:
-
High-density polyethylene
- LDPE:
-
Low-density polyethylene
- LVRW:
-
Low-value recyclable waste
- NIR:
-
Near-infrared
- MDW:
-
Municipal domestic waste
- MV:
-
Machine vision
- PET:
-
Polyethylene terephthalate
- PP:
-
Polypropylene
- PS:
-
Polystyrene
- PVA:
-
Polyvinyl alcohol
- PVC:
-
Polyvinyl chloride
- SSL:
-
Source separation level
References
Balthasar D (2013) Apparatus, system and method for detecting matter. No. WO2013115650A1. https://patents.google.com/patent/WO2013115650A1/en
Gundupalli SP, Hait S, Thakur A (2017) A review on automated sorting of source-separated municipal solid waste for recycling. Waste Manage 60:56–74. https://doi.org/10.1016/j.wasman.2016.09.015
Harbeck H (2014) Inspection apparatus. No. WO2015063300A1. https://patents.google.com/patent/WO2015063300A1/en
He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M (2019) Bag of tricks for image classification with convolutional neural networks. In: CVPR 2019 - Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 558–567. https://openaccess.thecvf.com/content_CVPR_2019/papers/He_Bag_of_Tricks_for_Image_Classification_with_Convolutional_Neural_Networks_CVPR_2019_paper.pdf
IiMedia (2019) 2019–2021 China's renewable resources industry trends and trends analysis report. https://www.iimedia.cn/c400/66825.html. Accessed on 27th July 2021
Klokkerud A (2011) An apparatus and method for inspecting matter. No. WO2012039622A8. https://patents.google.com/patent/WO2012039622A8/en
Lu W, Chen J (2022) Computer vision for solid waste sorting: a critical review of academic research. Waste Manage 142:29–43. https://doi.org/10.1016/j.wasman.2022.02.009
Masoumi H, Safavi SM, Khani Z (2012) Identification and classification of plastic resins using near infrared reflectance spectroscopy. Int J Mech Ind Eng 6:213–220
Ministry of Ecology and Environment of the People's Republic of China (2020) 2020 Annual report on the prevention and control of environmental pollution by municipal solid waste in large and medium-sized cities. https://www.mee.gov.cn/ywgz/gtfwyhxpgl/gtfw/202012/P020201228557295103367.pdf. Accessed on 5th Aug 2021
Pellenc Selective Technologies (2019) The multi-material sorting machine for sorting and recycling centers. https://www.pellencst.com/products/mistral-connect-2. Accessed on 5th Aug 2021
Sarc R, Curtis A, Kandlbauer L, Khodier K, Lorber KE, Pomberger R (2019) Digitalisation and intelligent robotics in value chain of circular economy oriented waste management: a review. Waste Manage 95:476–492. https://doi.org/10.1016/j.wasman.2019.06.035
Sesotec GmbH (2020) Recycling sorting systems with conveyor belt. https://www.sesotec.com/emea/en/products/groups/recycling-sorting-systems-with-conveyor-belt. Accessed on 18th Nov 2020
Shuyan C, Baoshen C, Hong G, Jialong S (2020) Plastics environmental footprint in China—executive report. http://www.nrdc.cn/Public/uploads/2020-12-28/5fe941e197b58.pdf
Steinert GmbH (2016) UniSort BlackEye—sorting of bulk materials. https://steinertglobal.com/us/magnets-sensor-sorting-units/sensor-sorting/nir-sorting-systems/unisort-blackeye. Accessed on 18th Nov 2020
Suzhou Jono Environmental Technology (2020) Heavy-load intelligent sorting robot. https://www.jonogroup.cn/products/znfx/197.html. Accessed on 5th May 2022
Tomra System ASA (2019) Resourceful solutions for nearly every waste stream. https://www.tomra.com/en/sorting/recycling/tomra-solutions. Accessed on 10th Jan 2020
Xiao W, Yang J, Fang H, Zhuang J, Ku Y (2020) Classifying construction and demolition waste by combining spatial and spectral features. Proc Inst Civ Eng - Waste Resour Manag 173(3):79–90. https://doi.org/10.1680/jwarm.20.00008
Zhihong C, Hebin Z, Yan W, Yanbo W, Binyan L (2018) Multi-task detection system for garbage sorting base on high-order fusion of convolutional feature hierarchical representation. Technical Committee on Control Theory, Chinese Association of Automation. Proceedings of the 37th Chinese Control Conference 5426–5430. doi:https://doi.org/10.23919/ChiCC.2018.8483842
Zhu X, Hu H, Lin S, Dai J (2018) Deformable ConvNets v2: More deformable, better results. In: CVPR 2019 - Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 9308–9316. https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Deformable_ConvNets_V2_More_Deformable_Better_Results_CVPR_2019_paper.pdf
Funding
This research was financially supported by the Major Program of Industry and University Cooperation of Fujian Province (2021H6029), the Science and Technology Project of Xiamen (2021FCX012501190024), the Major Special Program of Science and Technology of Fujian Province (2020YZ017022), and the Key Technologies Research and Development Program of Shenzhen (JSGG20201103100601004).
Author information
Authors and Affiliations
Contributions
Tianchen Ji contributed to methodology, writing—original draft, and investigation. Huaiying Fang contributed to writing—review and editing, and resources. Rencheng Zhang contributed to conceptualization and supervision. Jianhong Yang contributed to project administration and funding acquisition. Lulu Fan contributed to formal analysis, visualization, and data curation. Jiantao Li contributed to software and validation.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary file1 (MP4 137668 kb)
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ji, T., Fang, H., Zhang, R. et al. Automatic sorting of low-value recyclable waste: a comparative experimental study. Clean Techn Environ Policy 25, 949–961 (2023). https://doi.org/10.1007/s10098-022-02418-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10098-022-02418-7