The prediction of carbonyl groups during photo-thermal and thermal aging of polymers using artificial neural networks

H. Maouz, L. Khaouane, S. Hanini, Y. Ammi, M. Laidi, H. Benimam


Abstract: Major advances in modeling and control are required to meet future technical challenges in polymers manufacturing. This work investigates the recent applications of artificial neural network (ANN) in modeling the carbonyl groups during photo-thermal and thermal aging of PE, LDPE, PP, PVC, PS and EPDM. A set of 2450 data points for carbonyl index (CI) contains 15 polymer systems which are 5 pure, 5 binary, 3 tertiary and 2 quaternary systems, and 577 data points for concentration of carbonyl ([CO])including 4 systems, within 1 pure, 1 binary, 1 tertiary and 1 quaternary system, were used to test the neural networks proficiency. For the most promising neural network models, the predicted carbonyl index and concentration of carbonyl values of the total dataset were compared to measured carbonyl index and concentration of carbonyl values; good correlations were found (R= 0.9471 for ANN1 and R= 0.9830 for ANN2). The root mean square errors for the total dataset were 0.0958 and 0.0291 mol/l for CI and [CO] respectively. The comparison between the first and the second model proves the importance of the common properties of polymers and their additives in order to distinguish them.

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