TY - JOUR
T1 - Improvement proposal to automate the process of sorting materials in the recycling sector using the Internet of Things (IoT)
AU - Auccapiña-Castillo, Yamila
AU - Montes-Gallo, Luciana
AU - Velásquez-Costa, José
N1 - Publisher Copyright:
© 2025 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2025
Y1 - 2025
N2 - Advances in manufacturing have driven the development of automated systems that improve efficiency in industrial processes such as recycling. In this context, the implementation of automation and sensor technologies in recycling plants is essential for the accurate classification of recyclable materials. Capacitive and inductive sensors allow metals and other non-metallic materials to be detected, optimizing waste segregation, which reduces operating costs and improves workplace safety by reducing manual intervention. A key aspect of this modernization is the integration of the Internet of Things (IoT), which transforms the monitoring and control of these systems. Through IoT platforms such as Blynk, it is possible to view data such as the weight and quantity of recycled materials in real time, improving supervision and facilitating quick decisions. The scalability of the system allows efficient recycling management in the face of increasing demands. Key indicators include a reduction in cycle time by 58.3%, a decrease in the error rate between 15-20%, and a classification efficiency of 94.7%. In addition, productivity increased by 73.16%, allowing the classification of 32 pieces per minute, compared to 18.48 for the manual method, and processing capacity grew by 50.8%, reaching 60 kg/hour. Together, automation and IoT offer a comprehensive solution to recycling challenges, improving sustainability and reducing the environmental impact of industrial processes, while optimizing resources.
AB - Advances in manufacturing have driven the development of automated systems that improve efficiency in industrial processes such as recycling. In this context, the implementation of automation and sensor technologies in recycling plants is essential for the accurate classification of recyclable materials. Capacitive and inductive sensors allow metals and other non-metallic materials to be detected, optimizing waste segregation, which reduces operating costs and improves workplace safety by reducing manual intervention. A key aspect of this modernization is the integration of the Internet of Things (IoT), which transforms the monitoring and control of these systems. Through IoT platforms such as Blynk, it is possible to view data such as the weight and quantity of recycled materials in real time, improving supervision and facilitating quick decisions. The scalability of the system allows efficient recycling management in the face of increasing demands. Key indicators include a reduction in cycle time by 58.3%, a decrease in the error rate between 15-20%, and a classification efficiency of 94.7%. In addition, productivity increased by 73.16%, allowing the classification of 32 pieces per minute, compared to 18.48 for the manual method, and processing capacity grew by 50.8%, reaching 60 kg/hour. Together, automation and IoT offer a comprehensive solution to recycling challenges, improving sustainability and reducing the environmental impact of industrial processes, while optimizing resources.
KW - Automation
KW - Efficiency
KW - IoT
KW - Recycling
UR - https://www.scopus.com/pages/publications/105019296904
U2 - 10.18687/LACCEI2025.1.1.974
DO - 10.18687/LACCEI2025.1.1.974
M3 - Artículo de la conferencia
AN - SCOPUS:105019296904
SN - 2414-6390
JO - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
JF - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
IS - 2025
T2 - 23rd LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2025
Y2 - 16 July 2025 through 18 July 2025
ER -