Experimental and artificial neural network approach for prediction of the thermal degradation behavior of sugarcane-based vulcanization additives in natural rubber compounds

Abstract

The use of natural additives in elastomeric compounds is gaining the special attention of researchers and industry due to their potential applications as environmentally friendly compounds and lower cost-related. Another important issue is the use of powerful mathematical tools to predict experimental results, which is crucial for saving cost and time. Artificial neural network (ANN) combined with other mathematical methods, such as surface response methodology (SRM), can guarantee reliability and faster response of the predicted data for similar materials or properties. The great advantage of the present method is the fast prediction of the analyzed property, in the present case, thermal degradation curves, at heating rates not experimentally tested. In this study, a modified activator from sugarcane bagasse was incorporated in different concentrations in natural rubber compounds, and the degradation behavior was simulated by ANN and SRM based on the experimental thermal degradation curves at different heating rates from the thermogravimetric analysis. The simulated results showed an outstanding agreement with the experimental ones, evidencing the importance of using ANN and SRM tools in the prediction of properties of elastomeric compounds.