A general neural network model co-driven by mechanism and data for the reliable design of gas-liquid T-junction microdevices

In recent years, many models have been developed to describe the gas-liquid microdispersion process, which mainly rely on mechanistic analysis and may not be universally applicable. In order to provide a more comprehensive model and, most significantly, to provide a model for design, we have established a general database of microbubble generation in T-junction microdevices, including 854 data points from 12 pieces of literature. A neural network model that combines mechanistic and data modeling is developed. By transfer learning, more accurate results can be obtained. Additionally, we have proposed a design method that enables a relative deviation of less than 5% from the expected bubble size. A new device was designed and prepared to confirm the reliability of the method, which can prepare smaller bubbles than other common T-junction devices. In this way, a general and universal database and model are established and a design method for a gas-liquid T-junction microreactor is developed. A neural network model based on a T-junction gas-liquid microdispersion database was developed and used to achieve good prediction and design performance.

Automated summary

By establishing a universal database and model, we have developed a neural network model that combines mechanistic and data modeling to predict and design the performance of gas-liquid microdispersion. We have also proposed a design method that controls the deviation of bubble size to be less than 5%.