Design of a Waste Classification System Using a Low Experimental Cost Capacitive Sensor and Machine Learning Algorithms

The management and classification of solid waste is one of the most important challenges worldwide. The objective is to design a basic waste classification system at the source using a low-cost experimental capacitive sensor and machine learning algorithms. For this, two types of sensor models were established (Traditional Model (MT) and Non-Traditional Model (MNT)), which were built with recyclable material and tested with different types of materials, in order to evaluate their behavior and sensitivity level. The results obtained demonstrated that the two sensors responded with acceptable sensitivity levels for each of the materials used as a test; however, the MNT was the one that generated the values with the greatest variability, an aspect that is deemed highly significant, because, thanks to this type of response to various types of materials, it facilitates the classification processes through the use of machine learning algorithms. Finally, the two prototypes of sensors manufactured can be considered of significant relevance for the development of more complex solutions, related to the classification and possible characterization of materials, when compared to the capacitive sensors found on the market, which only then allow us to identify if there is a presence or not of some object through adjustment by potentiometer, generating as a result a digital output. This aspect largely limits the use of commercial capacitive sensors to applications exclusively related to presence or level detection.