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International Journal of Management and Business Development

Open Access Peer Review International
Open Access

Circular Economy in Aerospace: Recycling Composites & Rare Metals

DOI https://doi.org/10.55640/ijmbd-v02i05-03
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Saketh Kumar Vishwakarma 1 ,
4 Advance Supplier Management-Manufacturing Agent, Bombardier., Kansas, USA

Abstract

The aerospace industry is uniquely positioned as a sustainable innovation that must address economic performance and environmental issues. The choice of lighter and more effective materials by the aircraft designers poses sustainability problems since tantalum, niobium, and cobalt important factors in aircraft manufacturing, are scarce. CFRPs and rare metals are challenging to recycle because of their composition and the fragmented recycling system. Transportation Logistic supply chain management needs to integrate circular economy models of resource reuse and recycling functions in åACDC to navigate the challenges they face. They need Apache Spark processing and Kafka streaming to achieve these efficiencies and address real-time event streaming at the aerospace recycling centers. Material recovery efficiency rates are one of the crucial functions that Artificial Intelligence helps to navigate; This system adds the benefits of regulatory compliance on addressing the functionality of operations. Specifically, the aerospace industry must adopt a new microservice-based system from the existing monolithic versions to address rising sustainability goals. AI application of circular economy in aerospace enables new designs of recycling that minimize resource consumption to address current laws that regulate the environment in the industry. Therefore, the aerospace industry requires significant investment in digital circularity and workforce to reap sustainability performance outcomes from data-oriented approaches to sustainability, and gain a competitive advantage through environmental stewardship.

Keywords

Circular Economy, Aerospace Recycling, Apache Spark, Apache Kafka, Artificial Intelligence, Composites Recycling, Rare Metals Recovery, Sustainability

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. http://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. http://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, 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. https://ijsra.net/content/role-notification-scheduling-improving-patient
📄 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.
📄 Mannocci, A. (2017). Data Flow Quality Monitoring in Data Infrastructures.
📄 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.
📄 Menon, P. (2022). Data Lakehouse in Action: Architecting a modern and scalable data analytics platform. Packt Publishing Ltd.
📄 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.
📄 Plaut, J. (1998). Industry environmental processes: beyond compliance. Technology in society, 20(4), 469-479.
📄 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.
📄 Ryzko, D. (2020). Modern big data architectures: a multi-agent systems perspective. John Wiley & Sons.
📄 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.
📄 Sardana, J. (2022). The role of notification scheduling in improving patient outcomes. International Journal of Science and Research Archive. 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 (IJCAC), 1(4), 1-24.
📄 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.
📄 Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International journal of management reviews, 9(1), 53-80.
📄 Sulkava, A. (2023). Building scalable and fault-tolerant software systems with Kafka.
📄 Swan, P. (1992). A road map to understanding export controls: national security in a changing global environment. Am. Bus. LJ, 30, 607.
📄 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.
📄 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.
📄 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, UCLA).
📄 Yusuf, S. A. (2010). An evolutionary AI-based decision support system for urban regeneration planning.
📄 Zorpas, A. A., & Inglezakis, V. J. (2012). Automotive industry challenges in meeting EU 2015 environmental standard. Technology in Society, 34(1), 55-83.

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