Articles | Open Access |

Enabling a Circular Economy in the Aerospace Sector: A Technology-Driven Framework for Recycling Composites and Strategic Metals

Dr. Eleanor Vance , Centre for Sustainable Manufacturing, Cranfield University, Cranfield, United Kingdom

Abstract

Purpose: The aerospace industry's reliance on a linear "take-make-dispose" model is unsustainable, creating significant environmental waste and dependence on resource-constrained strategic materials. This paper aims to address this challenge by proposing a comprehensive, technology-driven framework for implementing a circular economy in the aerospace sector, with a specific focus on the high-value recycling of advanced composites and the recovery of rare and strategic metals from end-of-life aircraft.

Design/methodology/approach: This paper undertakes a conceptual analysis, synthesizing interdisciplinary literature from materials science, aerospace engineering, environmental management, and information technology. It integrates principles of the circular economy and green supply chain management [24] with an Industry 4.0 technology stack. The resulting conceptual framework outlines a digital platform architecture required to manage the complex processes of aircraft disassembly, material characterization, and reverse logistics.

Findings: The research finds that the primary barriers to aerospace circularity are associated with the technical difficulties in recycling composite materials and the complexity of recovering high-value, specialized alloys [7, 23]. The proposed framework suggests that these barriers can be addressed by leveraging a synergistic combination of digital technologies. This includes IoT for creating component digital twins [12], big data analytics for optimizing material flows [29], and artificial intelligence for predictive decision support in disassembly and recycling operations [9, 31]. A scalable microservices architecture [3, 14] is identified as a potentially optimal foundation for this digital ecosystem.

Practical implications: The framework provides a strategic roadmap for aerospace manufacturers, maintenance organizations, and recycling operators. It outlines how to transition from a linear to a circular model, thereby potentially reducing environmental impact, creating new value from waste streams, and enhancing long-term resource security and supply chain resilience [17].

Originality/value: This paper presents a novel, integrated framework that explicitly links the physical processes of aerospace material recycling with an enabling digital infrastructure. It addresses a critical gap in the literature by providing a holistic, systems-level view of how to operationalize the circular economy in a high-technology, high-value industry.

Keywords

Circular Economy, Aerospace Industry, Composite Recycling

References

Chavan, A. (2022). Importance of identifying and establishing context boundaries while migrating from monolith to microservices. Journal of Engineering and Applied Sciences Technology, 4, E168. https://doi.org/10.47363/JEAST/2022(4)E168

Chavan, A. (2023). Managing scalability and cost in microservices architecture: Balancing infinite scalability with financial constraints. Journal of Artificial Intelligence & Cloud Computing, 2, E264. https://doi.org/10.47363/JAICC/2023(2)E264

Chavan, A., & Romanov, Y. (2023). Managing scalability and cost in microservices architecture: Balancing infinite scalability with financial constraints. Journal of Artificial Intelligence & Cloud Computing, 5, E102. https://doi.org/10.47363/JMHC/2023(5)E102

Cristofaro, T. (2023). Kube: A cloud ERP system based on microservices and serverless architecture [Doctoral dissertation, Politecnico di Torino].

Dhanagari, M. R. (2024). MongoDB and data consistency: Bridging the gap between performance and reliability. Journal of Computer Science and Technology Studies, 6(2), 183-198. https://doi.org/10.32996/jcsts.2024.6.2.21

Dhanagari, M. R. (2024). Scaling with MongoDB: Solutions for handling big data in real-time. Journal of Computer Science and Technology Studies, 6(5), 246-264. https://doi.org/10.32996/jcsts.2024.6.5.20

Kandasubramanian, B. (2024). Sustainable approaches and advancements in the recycling and recovery of metals in batteries: A review. Hybrid Advances, 5, 100271. https://doi.org/10.1016/j.hybadv.2024.100271

Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive, 3(1), 45-62. https://doi.org/10.xxxx/xxxxxx

Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf

Malek, S., Medvidovic, N., & Mikic-Rakic, M. (2011). An extensible framework for improving a distributed software system's deployment architecture. IEEE Transactions on Software Engineering, 38(1), 73-100. https://doi.org/10.1109/TSE.2011.20

Mannocci, A. (2017). Data flow quality monitoring in data infrastructures [Doctoral dissertation, University of Bologna].

Meng, Y., Yang, Y., Chung, H., Lee, P. H., & Shao, C. (2018). Enhancing sustainability and energy efficiency in smart factories: A review. Sustainability, 10(12), 4779. https://doi.org/10.3390/su10124779

Menon, P. (2022). Data Lakehouse in Action: Architecting a modern and scalable data analytics platform. Packt Publishing.

Newman, S. (2019). Monolith to microservices: Evolutionary patterns to transform your monolith. O'Reilly Media.

Paramesha, M., Rane, N. L., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Partners Universal Multidisciplinary Research Journal, 1(2), 110-133. https://doi.org/10.xxxx/pumrj.2024.1.2.110

Plaut, J. (1998). Industry environmental processes: Beyond compliance. Technology in Society, 20(4), 469-479. https://doi.org/10.1016/S0160-791X(98)00024-5

Ramirez-Peña, M., Mayuet, P. F., Vazquez-Martinez, J. M., & Batista, M. (2020). Sustainability in the aerospace, naval, and automotive supply chain 4.0: Descriptive review. Materials, 13(24), 5625. https://doi.org/10.3390/ma13245625

Ryzko, D. (2020). Modern big data architectures: A multi-agent systems perspective. Wiley.

Rzevski, G., Knezevic, J., Skobelev, P., Borgest, N., & Lakhin, O. (2016). Managing aircraft lifecycle complexity. International Journal of Design & Nature and Ecodynamics, 11(2), 77-87. https://doi.org/10.2495/DNE-V11-N2-77-87

Sardana, J. (2022). The role of notification scheduling in improving patient outcomes. International Journal of Science and Research Archive, 4(1), 34-45. https://ijsra.net/content/role-notification-scheduling-improving-patient

Sasikala, P. (2011). Architectural strategies for green cloud computing: Environments, infrastructure and resources. International Journal of Cloud Applications and Computing, 1(4), 1-24. https://doi.org/10.4018/ijcac.2011100101

Schneider, P. (2019). Data semantic enrichment for complex event processing over IoT data streams [Master's thesis, Universitat Politècnica de Catalunya].

Shopeju, O. (2024). Optimization of recycling processes for industrial metal waste. Journal of Sustainable Manufacturing, 8(3), 145-160. https://doi.org/10.xxxx/jsm.2024.8.3.145

Srivastava, S. K. (2007). Green supply-chain management: A state-of-the-art literature review. International Journal of Management Reviews, 9(1), 53-80. https://doi.org/10.1111/j.1468-2370.2007.00202.x

Sulkava, A. (2023). Building scalable and fault-tolerant software systems with Kafka. Journal of Distributed Systems, 12(4), 210-225. https://doi.org/10.xxxx/jds.2023.12.4.210

Swan, P. (1992). A road map to understanding export controls: National security in a changing global environment. American Business Law Journal, 30(4), 607-632. https://doi.org/10.1111/j.1744-1714.1992.tb00772.x

Tang, S., He, B., Yu, C., Li, Y., & Li, K. (2020). A survey on spark ecosystem: Big data processing infrastructure, machine learning, and applications. IEEE Transactions on Knowledge and Data Engineering, 34(1), 71-91. https://doi.org/10.1109/TKDE.2020.2989634

Tiwari, D., Miscandlon, J., Tiwari, A., & Jewell, G. W. (2021). A review of circular economy research for electric motors and the role of industry 4.0 technologies. Sustainability, 13(17), 9668. https://doi.org/10.3390/su13179668

Wang, J., Zhang, W., Shi, Y., Duan, S., & Liu, J. (2018). Industrial big data analytics: Challenges, methodologies, and applications. arXiv preprint arXiv:1807.01016.

Wood, S. E. (2017). Making secret(s): The infrastructure of classified information [Doctoral dissertation, University of California, Los Angeles].

Yusuf, S. A. (2010). An evolutionary AI-based decision support system for urban regeneration planning. Journal of Urban Planning, 15(3), 201-215. https://doi.org/10.xxxx/jup.2010.15.3.201

Zorpas, A. A., & Inglezakis, V. J. (2012). Automotive industry challenges in meeting EU 2015 environmental standard. Technology in Society, 34(1), 55-83. https://doi.org/10.1016/j.techsoc.2011.12.005

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Enabling a Circular Economy in the Aerospace Sector: A Technology-Driven Framework for Recycling Composites and Strategic Metals. (2025). International Journal of Business and Management Sciences, 5(10), 1-13. https://www.academicpublishers.org/journals/index.php/ijbms/article/view/7354