An Improved Optimization Model to Predict the MOR of Glulam Prepared by UF-Oxidized Starch Adhesive: A Hybrid Artificial Neural Network-Modified Genetic Algorithm Optimization Approach.

The purpose of the present article is to study the bending strength of glulam prepared by plane tree (Platanus Orientalis-L) wood layers adhered by UF resin with different formaldehyde to urea molar ratios containing the modified starch adhesive with different NaOCl concentrations. Artificial neural network (ANN) as a modern tool was used to predict this response, too. The multilayer perceptron (MLP) models were used to predict the modulus of rapture (MOR) and the statistics, including the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to validate the prediction. Combining the ANN and the genetic algorithm by using the multiple objective and nonlinear constraint functions, the optimum point was determined based on the experimental and estimated data, respectively. The characterization analysis, performed by FTIR and XRD, was used to describe the effect of the inputs on the output. The results indicated that the statistics obtained show excellent MOR predictions by the feed-forward neural network using Levenberg-Marquardt algorithms. The comparison of the optimal output of the actual values obtained by the genetic algorithm resulting from the multi-objective function and the optimal output of the values estimated by the nonlinear constraint function indicates a minimum difference between both functions.

Adaptive harmony search algorithm for mechanical performance optimization of properties of particleboard from cotton stalk.

In this study, the optimum conditions for manufacturing particleboard-based waste cotton stalks were evaluated to achieve a good performance of mechanical properties. The response surface methodology (RSM) is used to calibrate the experiment results based on input variables consisting of the weight ratio of melamine formaldehyde to urea-formaldehyde (MU) resins, shelling ratio (SR), and the proportion of cotton particles to poplar particle (CP) in the core layer. An adaptive harmony search (AHS) algorithm is offered to search the optimum constructing conditions of mechanical properties for the composite particleboard using two optimization models. The optimum conditions are evaluated using maximum performance of mechanical properties. Besides, the optimum conditions are searched based on the material cost of the mechanical properties of composite particleboard that are utilized in its constraints. The results showed that the RSM can provide a perfect prediction for the mechanical properties of particleboard. The AHS is successfully applied to optimize the composite conditions. In the first optimization application, the optimal point is obtained for input variables in composite as 21.91% MU, 37.10% SR, and 13.54% CP. However, in the second condition, the optimum conditions are obtained for a good level as 18.32% MU, 51.71% SR, and 8.37% CP in the core layer.