Spray drying is an effective method to reduce shock sensitivity of energetic materials with the advantages of integrating atomization, drying, crystallization, and coating in one step. However, with the complex hydrodynamics and crystal growth process during spray drying, the development of theoretical models is very difficult. Therefore, five types of artificial neural network (ANN) models are proposed to accurately predict the mean particle size of energetic materials prepared by spray drying, such as Cascade-forward back propagation neural network (CFBP), Elman-forward back propagation neural network (EFBP), Feed-forward back propagation neural network (FFBP), Generalized regression neural network (GR) and Radial basis neural network (RB). The model input parameters are the inlet temperature (T), the liquid flow rate (L), the gas flow rate (G), the mass fraction (w), the molecular weight (M), and the surface tension (δ). The output parameter is the mean particle sizes (dp). To further illuminate the superior performance of ANN model, the effects of temperature, liquid flow rate, gas flow rate, mass fraction, and surface tension on the mean particle size are conducted. The ANN model of mean particle size for the energetic materials prepared by spray drying could be much useful for further improving its property.

中文翻译：

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喷雾干燥制备高能材料的ANN模型

喷雾干燥是一种降低含能材料冲击敏感性的有效方法，具有集雾化、干燥、结晶、包覆于一体的优点。然而，由于喷雾干燥过程中流体动力学和晶体生长过程复杂，理论模型的开发非常困难。因此，提出了五种类型的人工神经网络（ANN）模型来准确预测喷雾干燥制备的含能材料的平均粒径，例如级联前向反向传播神经网络（CFBP）、Elman-前向反向传播神经网络（ EFBP）、前馈反向传播神经网络（FFBP）、广义回归神经网络（GR）和径向基神经网络（RB）。模型输入参数为入口温度（T）、液体流量（L）、气体流量（G）、质量分数 (w)、分子量 (M) 和表面张力 (δ)。输出参数是平均粒径 (dp)。为了进一步说明 ANN 模型的优越性能，进行了温度、液体流速、气体流速、质量分数和表面张力对平均粒径的影响。通过喷雾干燥制备的含能材料的平均粒径 ANN 模型对于进一步改善其性能非常有用。