Actively Exploring Informative Data for Smart Modeling of Industrial Multiphase Flow Processes

Accurate depiction of the process characteristics of dynamic multiphase flows using a data-driven model is a challenge in industrial practices. Collection of sufficient data is costly and cumbersome, and it is difficult to identify representative data efficiently. This article develops an active learning method to explore information from multiphase flow process data, thus facilitating smart process modeling and prediction. An index is proposed to describe the process dynamics and nonlinearity using a probabilistic model, facilitating determination of informative data. The subsequent absorption of these data into the training set enhances the model quality gradually. This is relevant especially for transitional regions exhibiting dynamic information. In addition, a simple and efficient criterion to judge the learning termination has been designed. Consequently, new representative data are explored and learned in a sequential manner. The experimental results of two industrial multiphase flows demonstrate the advantages of the proposed method.