Study on enhancing waste PVC management through predictive Machine Learning analysis of TGA and its economic benefits

Abstract

The onset of COVID-19 has led to a sudden surge of existing medical plastic wastes, majority of them made up of PVCs. Pyrolysis, a globally adopted waste management alternative, can be a crucial aid to handle the bulk plastic wastes which makes the identification of thermal behaviour of materials important to aid in the scale-up of pyrolytic processes. The study presents a novel approach of utilising regression-based algorithms- Support Vector Regression (SVR), Random Forest Regression (RFR) and K-Nearest Neighbour algorithms (KNN), to predict the weight loss % at a heating rate of 20 K/min for 6 different PVC based medical plastic wastes. The correlation between the factors affecting Thermogravimetric Analysis (TGA) have been identified using heat maps, the Machine Learning (ML) models were trained on this TGA data and the model efficiencies were identified using performance metrics of Mean Squared Error (MSE), Mean Absolute Error (MAE) and R2. Several kinetic parameters like activation energy and reaction order were estimated using Coats - Redfern method followed by a cost analysis. The results showed that experimental and predicted weight loss were in good agreement with a regression R2 value > 0.97 for all the materials except the composite outer cover. Coats - Redfern method was successful in the estimation of kinetic parameters and the economic analysis indicated that the utilisation of ML in TGA analysis can have significant financial benefits for the concerned industry.