TY - JOUR
T1 - A machine learning based classification models for plastic recycling using different wavelength range spectrums
AU - Carrera, Berny
AU - Piñol, Victor Luid
AU - Mata, Judit Bazin
AU - Kim, Kwanho
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11/10
Y1 - 2022/11/10
N2 - Currently, the use and production of plastic are on the rising trend, leading to the increase in waste generation and consumption of raw materials, making it one of the most significant issues facing densely populated cities. As a result, plastic waste management is becoming one of society's primary concerns, as it has direct effects on the environment and sustainability of urban areas. However, the identification and classification of the different types of plastic remains a challenge, as current techniques still face limitations. Unfortunately, identification technologies cannot classify many types of plastic, and often a particular technology is used to classify certain types of plastic. In this study, we propose a classification methodology that analyses the use of different machine learning algorithms based on the infrared spectrum of polymers. This proposed classification methodology will be able to identify spectrums obtained from different wavelength ranges, including near-infrared spectroscopy (NIR) and mid-infrared spectroscopy (MWIR). Moreover, this study proposes a solution to the classification of black plastic, as well as common recycled plastics such as Polypropylene (PP), Polystyrene (PS), Polyethylene (PE), Polyvinyl chloride (PVC), and Polyethylene terephthalate (PET). Experiments were conducted using eleven machine learning classification algorithms, and a comparative analysis of their performance was presented. The results indicate that five out of eleven classifiers achieved over 95% on the four metrics analyzed (accuracy, precision, recall and f1-score), with the Multi-layer Perceptron (MLP) Classifier achieving 99.72% accuracy, 99.35% precision, 99.82% recall, and 99.58% f1-score.
AB - Currently, the use and production of plastic are on the rising trend, leading to the increase in waste generation and consumption of raw materials, making it one of the most significant issues facing densely populated cities. As a result, plastic waste management is becoming one of society's primary concerns, as it has direct effects on the environment and sustainability of urban areas. However, the identification and classification of the different types of plastic remains a challenge, as current techniques still face limitations. Unfortunately, identification technologies cannot classify many types of plastic, and often a particular technology is used to classify certain types of plastic. In this study, we propose a classification methodology that analyses the use of different machine learning algorithms based on the infrared spectrum of polymers. This proposed classification methodology will be able to identify spectrums obtained from different wavelength ranges, including near-infrared spectroscopy (NIR) and mid-infrared spectroscopy (MWIR). Moreover, this study proposes a solution to the classification of black plastic, as well as common recycled plastics such as Polypropylene (PP), Polystyrene (PS), Polyethylene (PE), Polyvinyl chloride (PVC), and Polyethylene terephthalate (PET). Experiments were conducted using eleven machine learning classification algorithms, and a comparative analysis of their performance was presented. The results indicate that five out of eleven classifiers achieved over 95% on the four metrics analyzed (accuracy, precision, recall and f1-score), with the Multi-layer Perceptron (MLP) Classifier achieving 99.72% accuracy, 99.35% precision, 99.82% recall, and 99.58% f1-score.
KW - Classification
KW - Data analysis
KW - Machine learning
KW - Plastic waste recycling
KW - Time-series analysis
KW - Wavelength
UR - http://www.scopus.com/inward/record.url?scp=85137732040&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2022.133883
DO - 10.1016/j.jclepro.2022.133883
M3 - Article
AN - SCOPUS:85137732040
SN - 0959-6526
VL - 374
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 133883
ER -