ABSTRACT A comparative approach was carried out between artificial neural networks (ANNs) and response surface methodology (RSM) to optimize the drying parameters during infrared–convective drying of white mulberry. The drying experiments were performed at different air temperatures (40°C, 55°C, and 70°C), air velocities (0.4, 1, and 1.6 m/s), and three levels of infrared radiation power (500, 1000, and 1500 W). RSM focuses on the maximization of effective moisture diffusivity ( ) and minimization of specific energy consumption ( ) in the drying process. The optimized conditions were encountered for the air temperature of 70°C, the air velocity of 0.4 m/s, and the infrared power level of 1464.57 W. The optimum values of and were 1.77 × 10−9 m2/s and 166.554 MJ/kg, respectively, with the desirability of 0.9670. Based on the statistical indices, the results showed that the feed and cascade-forward back-Propagation neural systems with application of Levenberg-Marquardt training algorithm and topologies of 3–20-20-1 and 3–10-10-1 were the best neural models to predict and , respectively. This finding suggests that the ANN as an intelligent method with better performance compared to the RSM can be used to predict the drying parameters of the infrared-convective drying of white mulberry fruit.

中文翻译：

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利用响应面法优化白桑树果实的红外对流干燥并通过人工神经网络建立预测模型

摘要 在人工神经网络 (ANN) 和响应面法 (RSM) 之间进行了比较方法，以优化白桑树红外对流干燥过程中的干燥参数。干燥实验在不同的空气温度（40°C、55°C 和 70°C）、空气速度（0.4、1 和 1.6 m/s）和三个级别的红外辐射功率（500、1000、和 1500 瓦）。RSM 专注于干燥过程中有效水分扩散率 ( ) 的最大化和比能耗 ( ) 的最小化。优化条件是空气温度为 70°C，风速为 0.4 m/s，红外功率为 1464.57 W。 和 的最佳值为 1.77 × 10−9 m2/s 和 166.554 MJ/ kg，分别具有 0.9670 的合意性。根据统计指标，结果表明，应用 Levenberg-Marquardt 训练算法和 3-20-20-1 和 3-10-10-1 拓扑的前馈和级联前向反向传播神经系统是预测和的最佳神经模型，分别。这一发现表明，与 RSM 相比，ANN 作为一种具有更好性能的智能方法可用于预测白桑果红外对流干燥的干燥参数。