Version 1
: Received: 9 October 2023 / Approved: 10 October 2023 / Online: 10 October 2023 (10:57:35 CEST)
How to cite:
Vesga Ferreira, J.C.; Sepulveda, F.A.A.; Perez Waltero, H.E. Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia. Preprints2023, 2023100628. https://doi.org/10.20944/preprints202310.0628.v1
Vesga Ferreira, J.C.; Sepulveda, F.A.A.; Perez Waltero, H.E. Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia. Preprints 2023, 2023100628. https://doi.org/10.20944/preprints202310.0628.v1
Vesga Ferreira, J.C.; Sepulveda, F.A.A.; Perez Waltero, H.E. Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia. Preprints2023, 2023100628. https://doi.org/10.20944/preprints202310.0628.v1
APA Style
Vesga Ferreira, J.C., Sepulveda, F.A.A., & Perez Waltero, H.E. (2023). Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia. Preprints. https://doi.org/10.20944/preprints202310.0628.v1
Chicago/Turabian Style
Vesga Ferreira, J.C., Faver Adrian Amorocho Sepulveda and Harold Esneider Perez Waltero. 2023 "Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia" Preprints. https://doi.org/10.20944/preprints202310.0628.v1
Abstract
The management and classification of solid waste is one of the most important challenges around the world, in order to sustain economic growth and preserve the environment. The objective is to propose the use of Smart Ecological Points as a strategy to address the problem related to the solid waste management system at the source, which has become one of the biggest problems globally and Colombia is no exception. The article describes the current state of the problem in the country and in turn, presents the possibility of developing a prototype corresponding to a low-cost Smart Ecological Point supported by the use of an experimental capacitive sensor and Machine Learning algorithms, which will reduce the time necessary for the classification of recyclable and non recyclable waste, minimizing the health risks and increasing the percentage of waste that can be reused, by reducing the probability of being contaminated at the source, an aspect that is very common when done manually. According to the results obtained, it was evident that the proposed prototype made an adequate classification of waste, generating the possibility of being manufactured with existing technology in the environment, in order to promote adequate waste classification at the source.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
