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. According to the results obtained, it was possible to show that the two sensors responded with adequate levels of sensitivity for each of the materials used as a test, however, the MNT model was the one that generated the values with the greatest variability, an aspect that is considered of great relevance, 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 great importance for the development of more complex solutions, related to the classification and possible characterization of materials, in comparison with the capacitive sensors found on the market, which only they allow to identify if there is 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.