Machine Learning for Two-Phase Flow Separation in a Liquid–Liquid Interface Manipulation Separator

Abstract

Two-phase flow separation is a key step in various downstream purification processes. The use of a separator with controllable flow behavior is recommended to avoid contamination. In this study, a core-annular separator for biphasic flow separation with four different chemical polarities was developed, and two machine learning-based methods were proposed for answering two emergent questions to meet real industrial needs. (1) Could complete two-phase separation be achieved under these operating conditions? (2) Could the separation process be accelerated by determining the maximum input flow rate of the water? Process prediction for automation, machine learning-based classifiers, and multilayer perceptron were used to address these questions by predicting successful separation and the maximum input flow rates of unknown water−solvent systems with limited experimental data as training samples. The core-annular separator achieved complete two-phase water−solvent separation at a maximum total input flow rate of 4000 μL min −1 . Moreover, the classification accuracy for complete separation reached 92.2%, and the multilayer perceptron network had the best performance for predicting the flow rate. This liquid−liquid interface manipulation separator and machine learning method could decrease the cost of relevant process development.

Performance of different microfluidic devices in continuous liquid-liquid separation

“…each application, the available design parameters are x and , which must be determined experimentally. A detailed description and diagrams can be found in [25,29,32].…”

“…This separation process initiates within the initial few millimeters of the helix wire, with the remainder of the wire providing structural support to ensure thorough separation. Comprehensive elucidation and schematics are accessible in references [25,29].…”

“…This arrangement is instrumental in perpetually propelling the carrier phase and integrating the discrete phase through the modulation of interfacial pressure. A distinctive advantage of this separator is its wire gaps, which are substantially larger than the pores present in separators based on capillary and membrane technologies, enabling the efficient separation of solid-bearing and high-viscosity fluid streams [25][26][27][28][29].…”

Physical information-enhanced graph neural network for predicting phase separation

“…In recent decades, the rapid development of machine learning has provided a new way of thinking for the realistic modeling of phase separation processes. [20][21][22][23][24][25] Since the past decade, machine learning has been used to solve classic problems in complex physical systems. [26][27][28][29][30][31][32][33][34] In particular, the graph network-based simulators show extraordinary predictive capabilities for physical systems such as fluids and rigid solids, thanks to the ability of graph networks to effectively capture relational information and structural properties in the system.…”