Machine learning to enhance sustainable plastics: A review

被引:1
|
作者
Guarda, Catia [1 ]
Caseiro, Joao [1 ]
Pires, Ana [1 ,2 ]
机构
[1] Centimfe Technol Ctr Mouldmaking Special Tooling &, Marinha Grande, Portugal
[2] Univ Nova Lisboa, Fac Ciencias & Tecnol, MARE Marine & Environm Sci Ctr, P-2829516 Caparica, Portugal
关键词
Machine learning; Sustainable plastics; Life cycle; Neural networks; Random forest; OPPORTUNITIES; PROGRESS; PLA;
D O I
10.1016/j.jclepro.2024.143602
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Plastic pollution requires advances in the production, use, and recovery of plastics to minimize environmental and human-health impacts. Machine Learning (ML) has been applied to accelerate the replacement of existing plastics with sustainable plastics. However, a comprehensive overview of how ML has been applied to promote sustainable plastics from a life cycle perspective is lacking. This article reviews the current literature on ML and its applications in sustainable polymers, representing a significant departure from previous knowledge. A comprehensive and systematic understanding of ML applications in the sustainable-plastic life cycle is provided by analyzing 47 articles on the subject published between 2019 and 2024. This review aims to increase knowledge of ML methods that are used to enhance sustainable plastics and to highlight the various challenges and opportunities. The findings revealed that ML has been applied at every stage of the polymer life cycle, with a higher incidence in the end-of-life and product-manufacturing stages. The application of ML was lowest in the assessment of the environmental impact of plastics. Neural networks and random forests are the most widely used algorithms because of their ability to deal with complex data patterns. Challenges must be addressed to increase the use of ML, namely the polymer complexity and interdependency of the polymer life cycle, the scarcity and low quality of data, and the validation of results by plastics value-chain specialists to increase trustability.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management
    Amari, Abdelfattah
    Ali, Mohammed Hasan
    Jaber, Mustafa Musa
    Spalevic, Velibor
    Novicevic, Rajko
    MEMBRANES, 2023, 13 (01)
  • [32] Sustainable materials alternative to petrochemical plastics pollution: A review analysis
    Singh N.
    Ogunseitan O.A.
    Wong M.H.
    Tang Y.
    Sustainable Horizons, 2022, 2
  • [33] Machine learning and process systems engineering for sustainable chemical processes-A short review
    Torres, Ana Ines
    Ferreira, Jimena
    Pedemonte, Martin
    CURRENT OPINION IN GREEN AND SUSTAINABLE CHEMISTRY, 2025, 51
  • [34] A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
    Sharma, Rohit
    Kamble, Sachin S.
    Gunasekaran, Angappa
    Kumar, Vikas
    Kumar, Anil
    COMPUTERS & OPERATIONS RESEARCH, 2020, 119
  • [35] Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research
    Jamwal, Anbesh
    Agrawal, Rajeev
    Sharma, Monica
    Kumar, Anil
    Kumar, Vikas
    Garza-Reyes, Jose Arturo Arturo
    JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT, 2022, 35 (02) : 566 - 596
  • [36] The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review
    Fatemeh Arjmandnia
    Ehsan Alimohammadi
    Patient Safety in Surgery, 18
  • [37] The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review
    Arjmandnia, Fatemeh
    Alimohammadi, Ehsan
    PATIENT SAFETY IN SURGERY, 2024, 18 (01)
  • [38] AN INNOVATIVE METHOD OF LEARNING AUTOMATION USING A MACHINE FOR STAMPING PLASTICS
    Kosa, Patrik
    Olejar, Martin
    Palkova, Zuzana
    10TH INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION (ICERI2017), 2017, : 1325 - 1331
  • [39] Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
    Wang, Shiqi
    Xia, Peng
    Chen, Keyu
    Gong, Fuyuan
    Wang, Hailong
    Wang, Qinghe
    Zhao, Yuxi
    Jin, Weiliang
    JOURNAL OF BUILDING ENGINEERING, 2023, 80
  • [40] Exploring machine learning applications in chemical production through valorization of biomass, plastics, and petroleum resources: A comprehensive review
    Mafat, Iradat Hussain
    Surya, Dadi Venkata
    Sharma, Sumeet K.
    Rao, Chinta Sankar
    Journal of Analytical and Applied Pyrolysis, 2024, 180