Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia

Juan Carlos Vesga Ferreira (), Faver Adrian Amorocho Sepulveda and Harold Esneider Perez Waltero
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Juan Carlos Vesga Ferreira: School of Basic Sciences, Technology and Engineering (ECBTI), Telecommunications Engineering Program, Universidad Nacional Abierta y a Distancia, Bogotá 111321, Colombia
Faver Adrian Amorocho Sepulveda: School of Basic Sciences, Technology and Engineering (ECBTI), Telecommunications Engineering Program, Universidad Nacional Abierta y a Distancia, Bogotá 111321, Colombia
Harold Esneider Perez Waltero: School of Basic Sciences, Technology and Engineering (ECBTI), Telecommunications Engineering Program, Universidad Nacional Abierta y a Distancia, Bogotá 111321, Colombia

Sustainability, 2024, vol. 16, issue 13, 1-20

Abstract: Around the world, managing and classifying solid waste is one of the most important challenges to sustaining economic growth and preserving the environment. The objective of this paper is to propose the use of Smart Ecological Points as a strategy to address the problem of solid waste management systems at the source, which has become one of the biggest problems globally, and Colombia is no exception. This article describes the current state of the problem in the country and presents a prototype of 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, increasing the percentage of waste that can be reused and minimizing health risks by reducing the probability of being contaminated at the source, an aspect that is very common when waste is sorted manually. According to the results obtained, it is evident that the proposed prototype made an adequate classification of waste, generating the possibility of it being manufactured with existing technology in order to promote adequate waste classification at the source.

Keywords: solid waste management; waste classification; capacitive sensor; machine learning; ecological point (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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