Prediction of nanofibrillated cellulose reinforced acetylated papers properties using artificial neural networks

The strength and barrier properties are some of the most important required features for kinds of paper, especially printing and packaging paper. So one of the important research areas in the paper industry is researching to improve these properties. In this research for the first time, the artificial neural networks (ANNs) were used to predict the strength and barrier properties of nanofibrillated cellulose reinforced acetylated papers.
Nanofibrillated cellulose (NFC) was produced from bleached commercial pulp using grinding method. Paper modification was performed using two methods including acetylation of pulp fibers before paper-sheet making, and acetylation of made paper-sheet. Pulp and paper acetylation process was performed in liquid phase at 70 °C for 0.5, 1, and 3 hours. The success of chemical modification was confirmed using Infrared spectroscopy. Two kinds of paper (unmixed and mixed paper) were made. The paper properties, including thickness, basis weight, bulk, breaking length, tear strength, and water barrier property were measured. In order to design an artificial neural network, the type of treatment (treatment of fibers and paper treatment), treatment time (0.5, 1, and 3 h), and type of paper (unmixed and mixed paper) were considered as input data, and the physical, mechanical, and barrier properties of the paper (bulk, breaking length, tear index, and water absorption) were considered as output data.
Among all the papers, the weakest strength and barrier properties were obtained for the paper made from the acetylated pulp. The best paper properties were obtained by the acetylation of paper. According to the results, the acetylation of paper had no significant effect on the physical and mechanical properties of produced papers (p>0.05). Acetylation led to decrease in the water absorption of unmixed and mixed papers about 24.5 and 48%, respectively.