In this study, the hydration behavior of gypsum paste mixed with bagasse and kenaf fibers as lignocellulosic material and fiberglass as inorganic material is evaluated. Moreover, the properties of gypsum-bonded fiberboard (GBFB) are examined using bagasse fibers (Saccharum officinarum.L), kenaf fibers (Hibiscus cannabinus.L) and industrial fiberglass. The weight ratios of fiberglass (at three levels 0, 3 and 6%), bagasse fiber (at three levels 0, 7.5 and 15%) and kenaf fiber (at three levels 0, 7.5 and 15%) to gypsum are used to make the gypsum-bonded fiberboard with the nominal density 1.10 g.cm^-3 . After preparing the fiberboard, its flexural properties were examined. Response surface methodology (RSM) and artificial neural network (ANN) were used to model the bending strength of gypsum-bonded fiberboard. According to the hydration tests, it was determined that as the extractives in the lignocellulosic materials increased, the temperature of the mixture decreased and its setting time increased. According to the bending test results, it was determined that there is an ideal consistency between the predicted values and the observed data, so that as bagasse and kenaf fiber increased, the modulus of rupture (MOR) increased. Maximum MOR of panel was predicted to be 10.81 MPa and 11MPa by RSM and ANN at optimum condition. Based on the statistical analysis, the training and validation data sets of the studied models were compared by the coefficient of determination (R²), root mean squares error (RMSE) and mean absolute error (MAE). ANN model showed a much more accurate prediction than RSM in terms of the values R², RMSE and MAE.

The present work was conducted to study the effects of wood species, particle size and residue particle size obtained from trimming of wood–cement composites on physical and mechanical properties of cement-bonded particleboard (CBPB). Particleboard was manufactured with a wood/cement ratio of 1:3 and specific gravity 1200 kg m⁻³. After manufacturing, the boards were tested. The second order plan was used to test the significant difference between factors and levels. It was shown that slenderness and compaction ratio increased and bulk density and specific surface decreased with the increase of particle size. With the increase in slenderness ratio and compaction ratio and decrease in bulk density and specific surface, thickness swelling and mechanical properties improved, but water absorption by the board increased. The addition of 6% of 5/3 fraction size of particle obtained from trimming of boards improved significantly the properties of the boards. The optimized panel properties, obtained using poplar particles with a fraction size of 7pass/on5, exceeded the BISON type HZ and EN Standard for Wood Particleboard. CBPB made of alder or poplar particles with 5/3 fraction size of residue exceeded the BISON type HZ. All CBPB with 5/3 fraction size of residue showed lower mean values of thickness swelling, well below the maximum requirements of both standards. In addition, wood species, fraction size of particles and residue size are believed to have been the main cause of change in the properties of the boards.

The present study evaluates and compares predictions on the performance and the approaches of the response surface methodology (RSM) and the artificial neural network (ANN) so to model the bending strength of the polyurethane foam-cored sandwich panel. The effect of the independent variables (formaldehyde to urea molar ratio (MR), sandwich panel thickness (PT) and the oxidized protein to melamine-urea-formaldehyde synthesized resin weight ratio (WR)) was examined based on the bending strength by the central composite design of the RSM and the multilayer perceptron of the ANN. The models were statistically compared based on the training and validation data sets via the determination coefficient (R²), the root mean squares error (RMSE), the absolute average deviation (AAD) and the mean absolute percentage error (MAPE). The R² calculated for the ANN and the RSM models was 0.9969 and 0.9960, respectively. The models offered good predictions; however, the ANN model was more precise than the RSM model, thus proving that the ANN and the RSM models are valuable instruments to model and optimize the bending properties of the sandwich panel.