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Exposing waxy oils to an electric field may significantly improve their cold flowability. Our previous study has shown that interfacial polarization, i.e., charged particle accumulation on the wax particle surface, is the primary mechanism of the electrorheological behavior of waxy oils. However, the way that charged particles interact with wax particles under an electric field remains unknown. In this study, we found no viscosity and impedance change for two waxy crude oils after their exposure to a high-voltage electric field. However, the yield stresses were reduced obviously. We thus proposed that the collision of colloidal particles such as resins and asphaltenes with the wax particles could be an essential mechanism that the wax particle structure was weakened. To verify this hypothesis, a series of ad hoc experiments were carried out, i.e., by performing electrorheological tests on model waxy oils containing additives removable under an electric field, including electrically-neutral colloidal particles (Fe3O4), charged colloidal particles (resins), and oil-soluble electrolyte (C22H14CoO4), respectively, and demonstrated that upon application of a high-voltage electric field, charged particles in a waxy oil may move and thus collide with wax particles, and consequently adhere to the wax particle surface. The particle collision results in damage to the wax particle network, and the electrostatic repulsion arising from the adhesion of the charged particle on the wax particle diminishes attraction between wax particles. This study clarifies the process of interfacial polarization. [ABSTRACT FROM AUTHOR]

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments, and the velocities and pressure drops data labeled by the corresponding flow regimes are collected. Combined with the flow regimes data of other GLCC positions from other literatures in existence, the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes. The choosing of input data types takes the availability of data for practical industry fields into consideration, and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification, including the typical ensemble models, SVM, KNN, Bayesian Model and MLP. The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning. Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more. For the pressure drops as the input of each algorithm, it is not the suitable as gas and liquid velocities, and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately. The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes, the flow regimes map, and the principles of algorithms. The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed. [ABSTRACT FROM AUTHOR]