Open-sided draft tubes provide an optimal gas distribution through a cross flow pattern between the spout and the annulus in conical spouted beds. The design, optimization, control, and scale-up of the spouted beds require precise information on operating and peak pressure drops. In this study, a multi-layer perceptron (MLP) neural network was employed for accurate prediction of these hydrodynamic characteristics. A relatively huge number of experiments were accomplished and the most influential dimensionless groups were extracted using the Buckingham-pi theorem. Then, the dimensionless groups were used for developing the MLP model for simultaneous estimation of operating and peak pressure drops. The iterative constructive technique confirmed that 4-14-2 is the best structure for the MLP model in terms of absolute average relative deviation (AARD%), mean square error (MSE), and regression coefficient (R²). The developed MLP approach has an excellent capacity to predict the transformed operating (MSE = 0.00039, AARD% = 1.30, and R² = 0.76099) and peak (MSE = 0.22933, AARD% = 11.88, and R² = 0.89867) pressure drops.

侧面开口的引流管通过锥形喷口中喷口和环空之间的交叉流动模式提供最佳的气体分配。喷动床的设计，优化，控制和按比例放大需要有关运行和峰值压降的精确信息。在这项研究中，采用多层感知器（MLP）神经网络来准确预测这些流体动力学特征。完成了相对大量的实验，并使用白金汉-皮定理提取了最具影响力的无量纲组。然后，将无量纲的组用于开发MLP模型，以同时估算运行和峰值压降。R ²）。发达的MLP方法具有出色的预测转换操作（MSE  =  0.00039，AARD％ =  1.30，R ²  =  0.76099）和峰值（MSE  =  0.22933，AARD％ =  11.88，R ²  =  0.89867）的能力。