From Trash to Cash: How Blockchain and Multi-Sensor-Driven Artificial Intelligence Can Transform Circular Economy of Plastic Waste?

Virgin polymers based on petrochemical feedstock are mainly preferred by most plastic goods manufacturers instead of recycled plastic feedstock. Major reason for this is the lack of reliable information about the quality, suitability, and availability of recycled plastics, which is partly due to lack of proper segregation techniques. In this paper, we present our ongoing efforts to segregate plastics based on its types and improve the reliability of information about recycled plastics using the first-of-its-kind blockchain smart contracts powered by multi-sensor data-fusion algorithms using artificial intelligence. We have demonstrated how different data-fusion modes can be employed to retrieve various physico-chemical parameters of plastic waste for accurate segregation. We have discussed how these smart tools help in efficiently segregating commingled plastics and can be reliably used in the circular economy of plastic. Using these tools, segregators, recyclers, and manufacturers can reliably share data, plan the supply chain, execute purchase orders, and hence, finally increase the use of recycled plastic feedstock.

[1]  S. Suh,et al.  Strategies to reduce the global carbon footprint of plastics , 2019, Nature Climate Change.

[2]  A. Solberg,et al.  Oil spill detection by satellite remote sensing , 2005 .

[3]  D. Hui,et al.  Recycling of plastic solid waste: A state of art review and future applications , 2017 .

[4]  M. Haward Plastic pollution of the world’s seas and oceans as a contemporary challenge in ocean governance , 2018, Nature Communications.

[5]  Toshihiro Fujita,et al.  Identification of plastics by infrared absorption using InGaAsP laser diode , 2001 .

[6]  K. Sankaran Protecting oceans from illicit oil spills: environment control and remote sensing using spaceborne imaging radars , 2019, Journal of Electromagnetic Waves and Applications.

[7]  K. Sankaran Carbon Emission and Plastic Pollution: How Circular Economy, Blockchain, and Artificial Intelligence Support Energy Transition? , 2020 .

[8]  T. Astrup,et al.  Quality Assessment and Circularity Potential of Recovery Systems for Household Plastic Waste , 2018, Journal of Industrial Ecology.

[9]  B. Caballero,et al.  Pyrolysis of plastic packaging waste: A comparison of plastic residuals from material recovery facilities with simulated plastic waste. , 2012, Waste management.

[10]  Jeannette M. García,et al.  Cleaning up plastic pollution in Africa , 2019, Science.

[11]  Xiaobo Qu,et al.  Blockchain Applications in Shipping, Transportation, Logistics, and Supply Chain , 2019, Smart Innovation, Systems and Technologies.

[12]  Steve Mansfield-Devine,et al.  Beyond Bitcoin: using blockchain technology to provide assurance in the commercial world , 2017 .

[13]  Ren C. Luo,et al.  Dynamic multi-sensor data fusion system for intelligent robots , 1988, IEEE J. Robotics Autom..

[14]  Robert E. Dvorak,et al.  Plastics recycling: challenges and opportunities , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Joseph Sarkis,et al.  Blockchain Practices, Potentials, and Perspectives in Greening Supply Chains , 2018, Sustainability.

[16]  N. Ngadi,et al.  Current state and future prospects of plastic waste as source of fuel: A review , 2015 .

[17]  C. Wilcox,et al.  Plastic waste inputs from land into the ocean , 2015, Science.

[18]  W. Kaminsky,et al.  Possibilities and limits of pyrolysis , 1992 .

[19]  Chelsea M. Rochman,et al.  Policy: Classify plastic waste as hazardous , 2013, Nature.

[20]  Huai N. Cheng,et al.  Plastics to fuel: a review , 2016 .

[21]  R. L. Waterland,et al.  Indentification of plastic waste using spectroscopy and neural networks , 1995 .

[22]  M. Fingas Oil Spills and Response , 2016 .

[23]  K. Sankaran Spaceborne radar remote sensing of ocean surfaces: electromagnetic modelling and applications , 2020 .

[24]  Richard C. Thompson,et al.  Accumulation and fragmentation of plastic debris in global environments , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[25]  Kim Ragaert,et al.  Mechanical and chemical recycling of solid plastic waste. , 2017, Waste management.

[26]  K. Sankaran,et al.  Radar remote sensing for oil spill classification (optimization for enhanced classification) , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).

[27]  A. Andrady,et al.  Future scenarios of global plastic waste generation and disposal , 2019, Palgrave Communications.

[28]  László Bartha,et al.  Thermal degradation of municipal plastic waste for production of fuel-like hydrocarbons , 2004 .

[29]  G. Jin,et al.  Plastic solid waste identification system based on near infrared spectroscopy in combination with support vector machine , 2019, Advanced Industrial and Engineering Polymer Research.

[30]  J. Jambeck,et al.  The Chinese import ban and its impact on global plastic waste trade , 2018, Science Advances.

[31]  Richard C. Thompson,et al.  Our plastic age , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[32]  Giovanni Schmid,et al.  Beyond Bitcoin: A Critical Look at Blockchain-Based Systems , 2017, Cryptogr..

[33]  D. R. Green,et al.  Quantitative Tar and Plastic Waste Distributions in the Pacific Ocean , 1974, Nature.

[34]  Joseph Sarkis,et al.  At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion , 2019, Applied Sciences.

[35]  W. Amelung,et al.  Plastics in soil: Analytical methods and possible sources. , 2018, The Science of the total environment.

[36]  R. Geyer,et al.  Production, use, and fate of all plastics ever made , 2017, Science Advances.

[37]  D M Scott,et al.  A two-colour near-infrared sensor for sorting recycled plastic waste , 1995 .

[38]  J. Derraik The pollution of the marine environment by plastic debris: a review. , 2002, Marine pollution bulletin.