Application of artificial neural networks for predicting tensile index and brightness in bleaching pulp

Abstract

The purpose of this study was to develop artificial neural network (ANN) models for predicting the effects of wood species, sodium perborate tetrahydrate (SPBTH) ratio, time, and beating degree on tensile index and brightness in bleaching pulp. Unbleached kraft-AQ bamboo and poplar pulps were exposed to first stage oxygen delignification for bleaching under 0,5 MPa, 3% NaOH and 12% consistency conditions. SPBTH bleaching was then carried out as the final stage. SPBTH bleached pulp was next beaten using two different degrees (55 SR° and 65 SR°). Tensile index and brightness data for training, validation and testing of the models were elicited from these experimental investigations. The models were established using the resulting data. The lowest R2 value was 0,98 among training, testing and validation data sets in the prediction of both tensile index and brightness. The networks therefore explain at least 98% of the experimental data for all data sets. The results indicate that ANN is a useful and effective tool for predicting tensile index and brightness. This study thus describes a novel and alternative approach to predicting tensile index and brightness in bleaching pulp compared to the literature.

Keywords

• Bleaching process
• neural network modeling
• Phyllostachys bambusoides
• Populus euramericana
• pulping properties.