journal article·2024-05Open access

Retrosynthetic Life Cycle Assessment: A Short Perspective on the Sustainability of Integrating Thermoplastics and Artificial Intelligence Into Composite Systems

Vahid Yaghoubi, Baris Kumru

Advanced Sustainable Systems· Volume 8· Issue 5

Abstract

Abstract Over the past 30 years, the polymer composite industry has flourished, producing advanced structural materials for the aviation, energy, and transportation sectors. However, the use of crosslinked thermoset matrices has been linked to significant end‐of‐life challenges, presenting a critical issue for the industry. Moreover, the industry is characterized by numerous labor‐intensive processes. In alignment with Industry 4.0 principles, two major routes have been identified to enhance sustainability: the utilization of high‐performance thermoplastic matrices and the integration of artificial intelligence in manufacturing. Nevertheless, there are substantial concerns regarding the life cycle assessment of these technologies, which are not accounted for in the initial calculations, including the environmental footprint of polymer synthesis and energy requirements for training AI. This perspective aims to address potential and significant CO2 emissions from chemical feedstocks and the high computing requirements of these new technologies.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: biodegradable polymer synthesis and properties · Recycling and Waste Management Techniques · Polymer crystallization and properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Aerospace Structures & Materials Department Faculty of Aerospace Engineering Delft University of Technology Delft 2629 HS The Netherlands

References (45)

Show source-provided references
1. 10.1007/s10443-012-9258-710.1007/s10443-012-9258-7 ↗
2. 10.1177/073168441453079010.1177/0731684414530790 ↗
3. 10.3390/polym1110166710.3390/polym11101667 ↗
4. 10.1021/acs.est.1c0546210.1021/acs.est.1c05462 ↗
5. 10.1002/macp.20230018610.1002/macp.202300186 ↗
6. 10.1080/0740817X.2011.59017710.1080/0740817X.2011.590177 ↗
7. 10.1039/D2MA00654E10.1039/D2MA00654E ↗
8. 10.1021/sc500087z10.1021/sc500087z ↗
9. 10.1016/j.compositesb.2020.10805310.1016/j.compositesb.2020.108053 ↗
10. 10.1016/j.cep.2011.09.00710.1016/j.cep.2011.09.007 ↗
11. 10.1016/S0142-9418(01)00017-410.1016/S0142-9418(01)00017-4 ↗
12. 10.1038/s41586-021-04350-010.1038/s41586-021-04350-0 ↗
13. 10.1016/j.compositesb.2023.11092410.1016/j.compositesb.2023.110924 ↗
14. 10.1016/S1359-835X(04)00182-410.1016/S1359-835X(04)00182-4 ↗
15. 10.1016/j.compstruct.2019.01.05810.1016/j.compstruct.2019.01.058 ↗
16. Apostolidis D. · RSC Appl. Polym. · 2024
17. 10.1002/pc.2029710.1002/pc.20297 ↗
18. 10.1016/j.ymssp.2021.10834110.1016/j.ymssp.2021.108341 ↗
19. 10.1016/j.compositesa.2022.10707410.1016/j.compositesa.2022.107074 ↗
20. 10.1002/pc.2713910.1002/pc.27139 ↗
21. 10.1002/pc.75010040910.1002/pc.750100409 ↗
22. 10.1002/admt.20180027110.1002/admt.201800271 ↗
23. 10.1002/pc.2680110.1002/pc.26801 ↗
24. 10.3390/aerospace811035110.3390/aerospace8110351 ↗
25. 10.1016/j.procir.2022.02.11510.1016/j.procir.2022.02.115 ↗
26. Verma A. · 2021 · 16
27. 10.1016/j.joule.2020.12.02710.1016/j.joule.2020.12.027 ↗
28. Maher K. · Stanford Human‐Centered Artif. Intell. · 2020
29. B. H. S.Bisht 2020.https://medium.com/analytics‐vidhya/types‐of‐convolutional‐neural‐networks‐lenet‐alexnet‐vgg‐16‐net‐resnet‐and‐inception‐net‐759e5f197580.
30. Gilani K. · AMB Crypto · 2023
31. Hao K. · MIT Technol. Rev. · 2019
32. D.Amodei D.Hernandez 2018.https://openai.com/research/ai‐and‐compute.
33. https://www.hyro.ai/glossary/gpt‐3/(accessed)
34. 10.3390/su1211467410.3390/su12114674 ↗
35. L.Columbus Roundup Of Internet Of Things Forecasts And Market Estimates 2016 https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup‐of‐internet‐of‐things‐forecasts‐and‐market‐estimates‐2016/?sh=430224ab292d.
36. L.Dignan IoT devices to generate 79.4ZB of data in 2025 2019 https://www.zdnet.com/article/iot‐devices‐to‐generate‐79‐4zb‐of‐data‐in‐2025‐says‐idc/(accessed).
37. Itoh S. · Proc. – IEEE/ACM Int. Work. Grid Comput. · 2010 · 1 · 305
38. 10.1016/j.iotcps.2023.02.00410.1016/j.iotcps.2023.02.004 ↗
39. Benmeziane H. · arXiv · 2021
40. Shariatzadeh S. M. · arXiv · 2023
41. 10.1016/j.knosys.2020.10662210.1016/j.knosys.2020.106622 ↗
42. 10.1126/science.abn729310.1126/science.abn7293 ↗
43. Supercharging new materials design with AI and Hybrid Cloud | IBM Research Blog https://research.ibm.com/blog/ibm‐molecule‐generation‐experience 2023.
44. Garcia D. P. · arXiv · 2022
45. Verma A. · 2021