This study aims at evaluating the dispersed phase holdup and Sauter mean drop diameter (d₃₂) of three liquid-liquid systems in an Oldshue–Rushton pilot column. The systems include butanol–water, n-butylacetate–water, and toluene–water with a wide range of interfacial tension. Two modeling approaches of response surface methodology (RSM) and artificial neural network (ANN) are employed. Four independent factors, including rotation rate (N: 60-240 rpm), continuous phase velocity (V_c: 0.499-0.997 mm/s), dispersed phase velocity (V_d: 0.499-0.997 mm/s), and interfacial tension ($\sigma$: 1.75-36 mN/m) are taken into account to investigate their individual and interaction impacts. Two correlations for both responses are developed according to the composite central design (CCD) method in RSM modeling. A python-based code, an open-source project NetHub, is utilized to apply a Multi-Layer Perceptron (MLP) algorithm in ANN modeling to find the best network structure with minimum mean square error (MSE) and maximum coefficient of determination (R²). The high prediction accuracy of both developed models is confirmed by the R² values of 0.9975 and 0.9905 for RSM and ANN modeling, respectively. The optimum network structure contained four layers with 15, 20, 10, and 2 neurons at each layer, respectively, achieving the minimum MSE value of 0.0023.

本研究旨在评估Oldshue-Rushton 中试塔中三种液-液系统的分散相滞留率和 Sauter 平均液滴直径 ( d ₃₂ )。该系统包括丁醇-水、乙酸正丁酯-水和甲苯-水，具有广泛的界面张力。采用响应面法（RSM）和人工神经网络（ANN）两种建模方法。四个独立因素，包括转速（N： 60-240 rpm）、连续相速度（V _c : 0.499-0.997 mm/s）、分散相速度（V _d : 0.499-0.997 mm/ s）s) 和界面张力 ($\sigma$: 1.75-36 mN/m ) 被考虑到调查它们的个体和相互作用影响。根据 RSM 建模中的复合中心设计 (CCD) 方法开发了两种响应的两个相关性。基于 python 的代码，一个开源项目 NetHub，被用来在 ANN 建模中应用多层感知器 (MLP) 算法，以找到具有最小均方误差 (MSE) 和最大决定系数 ( R ) 的最佳网络结构²）。R ²证实了两种开发模型的高预测精度RSM 和 ANN 建模的值分别为 0.9975 和 0.9905。最优网络结构包含四层，每层分别有 15、20、10 和 2 个神经元，达到最小 MSE 值 0.0023。