This paper reports the results of using multiple-ring microchannels for enhancing liquid-liquid extraction performance. The effects of geometrical parameters including ring and distance characteristics on the extraction efficiency were studied. The mass transfer performance was analyzed using Water + Alizarin Red S+1-octanol system. By change in geometrical parameters, the extraction efficiency of multiple-ring microchannels improved up to 62.9% compared with that of the plain one. The performance ratio is defined based on two contrary effects of friction factor and extraction efficiency for evaluating the extraction performance. A performance ratio of 1.5 was achieved that confirmed the advantage of using this type of microfluidic extraction system. Artificial neural network and adaptive neuro-fuzzy inference system were utilized to evaluate the performance ratio of the multiple-ring microchannels. The mean relative error values of the testing data were 0.397% and 0.888% for the neural network and the neuro-fuzzy system, respectively. The estimation accuracy for both models is appropriate, but the precision of the neural network id higher than that of the neuro-fuzzy system. The genetic algorithm approach was employed to develop a new empirical correlation for predicting the performance ratio with a mean relative error of 1.558%.

中文翻译：

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多环微通道液-液传质实验研究及人工智能建模

本文报告了使用多环微通道提高液-液萃取性能的结果。研究了包括环和距离特征在内的几何参数对提取效率的影响。使用水+茜素红S+1-辛醇系统分析传质性能。通过改变几何参数，多环微通道的提取效率比普通微通道提高了62.9%。性能比是基于摩擦系数和提取效率这两个相反的影响来定义的，用于评估提取性能。实现了 1.5 的性能比，这证实了使用这种类型的微流体提取系统的优势。利用人工神经网络和自适应神经模糊推理系统来评估多环微通道的性能比。神经网络和神经模糊系统的测试数据的平均相对误差值分别为 0.397% 和 0.888%。两种模型的估计精度都合适，但神经网络id的精度高于神经模糊系统的精度。采用遗传算法方法开发了一种新的经验相关性，用于预测平均相对误差为 1.558% 的性能比。但神经网络id的精度高于神经模糊系统的精度。采用遗传算法方法开发了一种新的经验相关性，用于预测平均相对误差为 1.558% 的性能比。但神经网络id的精度高于神经模糊系统的精度。采用遗传算法方法开发了一种新的经验相关性，用于预测平均相对误差为 1.558% 的性能比。