Abstract

In agitated systems, the phase inversion (PI) phenomenon – the mechanism by which a dispersed phase becomes the continuous one – has been studied extensively in an empirical manner, and few models have been put forward through the years. The underlying physics are still to be fully understood. In this work, the experimental evidence published in literature is used to train machine learning models that may infer the inherent rules that lead to a given dispersion type (O/W or W/O), as well as predict the value of the dispersed phase volume fraction at the edge of the inversion point. Decision trees, bagged decision trees, support-vector machines, and multiple perceptrons are implemented and compared. Results show that it is possible to infer an ensemble of physical rules that explain why a given dispersion is O/W or W/O, where a strong “turbulence constraint” is identified. The intuitive rule that PI occurs at 50% dispersed phase almost never holds. Moreover, neural networks have shown a better performance at predicting the PI point than the other algorithms tested. Finally, a theoretical study is performed in an effort to produce a phase inversion map with the relevant operating variables. This study showed a strong nonlinear effect of the impeller-to-vessel size ratio and an asymmetrical behavior of the interfacial tension on the phase inversion points.

摘要

在搅拌系统中，相转化 (PI) 现象 - 分散相变成连续相的机制 - 已经以经验的方式进行了广泛的研究，但多年来提出的模型很少。基础物理学仍有待完全理解。在这项工作中，文献中发表的实验证据用于训练机器学习模型，这些模型可能会推断出导致给定分散类型（O/W 或 W/O）的内在规则，以及预测分散相的值反演点边缘的体积分数。实现并比较了决策树、袋装决策树、支持向量机和多个感知器。结果表明，可以推断出一组物理规则来解释为什么给定的色散是 O/W 或 W/O，其中确定了一个强大的“湍流约束”。PI 出现在 50% 分散相的直观规则几乎不成立。此外，与其他测试算法相比，神经网络在预测 PI 点方面表现出更好的性能。最后，进行了理论研究，以产生具有相关操作变量的倒相图。该研究表明叶轮与容器尺寸比的强非线性效应和界面张力对相转化点的不对称行为。进行了一项理论研究，以产生具有相关操作变量的倒相图。该研究表明叶轮与容器尺寸比的强非线性效应和界面张力对相转化点的不对称行为。进行了一项理论研究，以产生具有相关操作变量的倒相图。该研究表明叶轮与容器尺寸比的强非线性效应和界面张力对相转化点的不对称行为。