Co-Attention-Based Cross-Stitch Network for Parameter Prediction of Two-Phase Flow

Gas-liquid two-phase flow is pervasive in industrial processes, and accurate measurement of its parameters can effectively improve industrial efficiency. Void fraction, a key parameter in gas-liquid two-phase flow, plays a crucial role in mixture density determination and flow structure analysis. In this article, a novel multitask learning (MTL) method is proposed to predict the gas void fraction. First, we conduct vertical upward gas-liquid two-phase flow experiments to obtain fluid signals relying on the four-sector distributed conductance sensor. Then, we pair the measurement of gas void fraction and its classification and design a co-attention-based cross-stitch network (CACSNet) to process these two closely related tasks simultaneously. In CACSNet, two parallel embedding branches are adopted to extract task-specific features. There are three feature extractors in each branch to capture in-depth representations of temporal and spatial. To learn an optimal combination of these task-specific features and generate shared representations, we propose cross-stitch embedding with attention modules (CEAMs), which is incorporated the channel co-attention module (CCAM) and the temporal co-attention module (TCAM). Finally, we compare our CACSNet with single-task baseline and other competitive methods, which proves that our framework achieves superior performance in gas void fraction prediction. We also conduct detailed model ablation and parameter analysis to illustrate the efficiency of our proposed structure.

Automated summary

This article proposes a novel multitask learning method for predicting gas void fraction. By conducting experiments and designing a co-attention-based cross-stitch network, the measurement and classification of gas void fraction are simultaneously processed. The results show that this method achieves superior performance in gas void fraction prediction.