Artificial neural network for predicting the performance of waste polypropylene plastic-derived carbon nanotubes

Abstract In this study, artificial neural network (ANN) model [function fitting neural network (FITNET)] was developed to describe the yield and quality of multi-walled carbon nanotubes (MWCNTs) deposited over NiMo/CaTiO3 catalyst using waste polypropylene plastics as cheap hydrocarbon feedstock using a single-stage chemical vapour deposition (CVD) technique. Experimental dataset was developed using user-specific design with four numeric factors (input variable): synthesis temperature, furnace heating rate, residence time and carrier gas (nitrogen) flow rate to control the performance (yield and quality) of produced CNTs. Levenberg-Marquardt algorithm was utilized in training, validating, and testing the experimental dataset. The predicted model gave considerable coefficient (R) value close to 1. The presented model would be of remarkable benefit to successfully describe and predict the performance of PP-derived CNTs and show how the predictive variables could affect the response variables (quality and yield) of CNTs.