Abstract
In this work, an artificial neural network (ANN) model was efficiently developed to predict the pyrolysis of mixed plastics, including pure polystyrene (PS), polypropylene (PP), low-density polyethylene (LDPE), and high-density polyethylene (HDPE), at a heating rate of 60 K/min using thermogravimetric analysis (TGA) data. The data of seventeen experimental tests of polymer mixtures with different compositions were used. A feed-forward back-propagation model, with 15 and 10 neurons in two hidden layers and TANSIG-TANSIG transfer functions, was constructed to predict the weight left percent during the pyrolysis of the mixed polymer samples. The model input variables include the composition of each polymer (PS, PP, LDPE, and HDPE), and temperature. The results showed an excellent agreement between the experimental and the predicted weight left percent values, where the correlation coefficient (R) is greater than 0.9999. In addition, to validate the proposed model, a highly efficient performance was found when the proposed model was simulated using new input data. Furthermore, a sensitivity analysis was performed using Pearson correlation to find the uncertainties associated with the relationship between the output and the input parameters. Temperature was found to be the most sensitive input parameter.