Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application

Pure polymers of polystyrene (PS), low-density polyethylene (LDPE) and polypropylene (PP), are the main representative of plastic wastes. Thermal cracking of mixed polymers, consisting of PS, LDPE, and PP, was implemented by thermal analysis technique thermogravimetric analyzer (TGA) with heating rate range (5-40 K/min), with two groups of sets: (ratio 1:1) mixture of PS and PP, and (ratio 1:1:1) mixture of PS, LDPE, and PP. TGA data were utilized to implement one of the machine learning methods, artificial neural network (ANN). A feed-forward ANN with Levenberg-Marquardt (LM) as learning algorithm in the backpropagation model was performed in both sets in order to predict the weight fraction of the mixed polymers. Temperature and the heating rate are the two input variables applied in the current ANN model. For both sets, 10-10 neurons in logsig-tansig transfer functions two hidden layers was concluded as the best architecture, with almost (R > 0.99999). Results approved a good coincidence between the actual with the predicted values. The model foresees very efficiently when it is simulated with new data.

Automated summary

In this study, thermal cracking of plastic wastes, including polystyrene (PS), low-density polyethylene (LDPE), and polypropylene (PP), was investigated using thermal analysis technique thermogravimetric analyzer (TGA). An artificial neural network (ANN) model was developed to predict the weight fraction of mixed polymers, and the results showed good agreement between the actual and predicted values, indicating the high efficiency of the model in simulating new data.