The effects of refining history and recycling times of NSSC pulp as a representative of semi-chemical pulps were studied. The results indicated that NSSC behaved as would be expected for a chemical pulp in all aspects. In fact, increasing the recycling cycles decreased the apparent density, tensile index, burst index, tear index, water retention value (WRV), and increased the hornification. In the current research, 400 mL CSF was judged to be the most suitable treatment among the refining levels considered. In the case of virgin pulp 400 mL CSF yielded better results than 500 mL CSF in all aspects (apparent density, tensile index, burst index, tear index, WRV and hornification). Also, there was not much difference with 300 mL CSF in these properties. Generally, a refining history of 400 mL CSF gave rise to the least negative influence on different properties compared to 500 and 300 mL CSF in 1 st , 2 nd , and 3 rd recycling cycles of NSSC.
The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.