Abstract: A fractional gas hold-up performance in mechanically agitated reactor (i.d. range is 0.382m to 1.16m) with mixed hydrofoil impeller 6k5 and Rushton turbine was presented. The effects of geometrical variables,including the ratio of impeller diameter,the distance between impellers,the clearance of lower impeller, gas sparger position, the baffle type,and the operating conditions such as agitator speed, the rate of gas, and the pumping mode of axial impeller 6k5 on fractional gas hold-up were investigated. With the dimensionless and dimensional methods, two correlations were proposed, but their prediction ability was not satisfactory.The correlation based on artificial neural network was established.It was able to predict fractional gas hold-up reasonably well and the relative mean error of generalization by this neural network model was within ±10%, if the parameters were in the trained range. The neural network model could be used for offline prediction and parameter optimization, and could be useful for scale-up because dimensionless parameters were used.

Key words:

翼型桨, 斜叶桨, 气含率, 机械搅拌, 挡板, 人工神经网络

摘要： 研究了内径φ0.382～1.16m机械搅拌釜中翼型组合桨气液两相的持气特性，考察了结构参数 (包括翼型桨径、桨间距、桨下距离、通气位置、挡板形式及翼型桨排出流方向)和操作参数 (包括搅拌转速与通气量)对气含率的影响.采用单位体积功率和表观气速及Froude数和通气流动数两种方法对气含率进行了关联处理，并采用神经网络技术建立了翼型组合桨的气含率关联的网络模型，该模型掌握了各参数对气含率的影响规律，具有很好的泛化能力，与传统关联方法相比，网络模型的预测误差可缩小至±10％以内.

关键词:

翼型桨, 斜叶桨, 气含率, 机械搅拌, 挡板, 人工神经网络