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.

在这项研究中，评估了用于制造刨花板基废棉秸秆的最佳条件，以实现良好的机械性能。响应面法（RSM）用于根据输入变量校准实验结果，这些变量包括三聚氰胺甲醛与脲醛（MU）树脂的重量比，脱壳率（SR）以及棉粒与杨树粒的比例（CP）在核心层。提供了一种自适应和声搜索（AHS）算法，使用两个优化模型来搜索复合刨花板的力学性能的最佳构造条件。使用最大的机械性能评估最佳条件。除了，根据复合刨花板机械性能的材料成本（在其约束条件下使用）来搜索最佳条件。结果表明，RSM可以为刨花板的力学性能提供理想的预测。AHS已成功应用于优化复合条件。在第一个优化应用程序中，获得复合材料中输入变量的最佳点，分别为21.91％MU，37.10％SR和13.54％CP。然而，在第二条件下，获得了最佳条件，以在芯层中获得良好水平的最佳水平，例如MU为18.32％，SR为51.71％，CP为8.37％。在第一个优化应用程序中，获得了复合材料中输入变量的最佳点，分别为21.91％MU，37.10％SR和13.54％CP。然而，在第二条件下，获得了最佳条件，以在芯层中具有良好的水平，如18.32％MU，51.71％SR和8.37％CP。在第一个优化应用程序中，获得复合材料中输入变量的最佳点，分别为21.91％MU，37.10％SR和13.54％CP。然而，在第二条件下，获得了最佳条件，以在芯层中获得良好水平的最佳水平，例如MU为18.32％，SR为51.71％，CP为8.37％。