RESUMO
Paper industry uses cationic polymers for imparting strong bonds with pulp furnish to enhance strength properties. Due to environmental reasons, emphasis is on utilization of biobased polymers in place of synthetic. Sugarcane bagasse, an agro-industrial waste, was processed for extraction of alpha cellulose and preparation of cationic derivative. Reaction conditions were optimized to achieve highly substituted cationic derivative with insertion of 2-hydroxy-3-(trimethylammonium) propyl group. Artificial neural network (ANN) was applied to analyze the experimental data for cationization modeling. Maximum degree of substitution 0.66, was achieved at 5.0 M NaOH/anhydro glucose unit (AGU), 20 °C alkalization temperature, 8 min alkalization time, 3.5 M/AGU etherification agent concentration, 45 min time and 60 °C etherification reaction temperature. The experimental results showed that mean square error values for input parameters were significantly low. The ANN based regression values of the output, and computed values of target were close to unity. ANN based fitting indicates better performance level to predict the degree of substitution. The synthesized cationic cellulose was characterized through FTIR, XRD, NMR, FESEM and TGA. The activity of cationized cellulose as wet-end additive was tested for bagasse, wheat straw and recycled pulps due to their shorten fiber and feeble pulp characters than wood pulp.