Soft measurement of oil–water two-phase flow using a multi-task sequence-based CapsNet

• Published in: ISA transactions Vol. 137; pp. 629 - 645
• Main Authors: OuYang, Lei, Jin, Ningde, Bai, Landi, Ren, Weikai
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2023
• Subjects: Capsule network; Multi-task learning; Oil–water flow; Soft measurement; Superficial velocity prediction; Oil–water flow; Soft measurement; Superficial velocity prediction; Capsule network; Multi-task learning
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/j.isatra.2022.12.007

Flow parameters measurement facilitates the understanding of two-phase flow. Due to the changeable structures of the flow, the prediction of superficial velocity of oil–water two-phase flow in large diameter pipes is still a challenging problem. Therefore, we first conducted a vertical upward oil–water two-phase flow experiment in a 125 mm ID pipe, and obtained the response signals under different flow conditions by a vertical multi-electrode array (VMEA) conductance sensor. Then, new data pre-processing (1D to 2D) techniques and information fusion techniques (network channels) are employed. Moreover, the front-end structure of the network is optimized using a combination of attention block and residual structure, and the middle structure is optimized using inception block; on the other hand, the back-end structure of the original capsule network is innovatively changed so that it can handle both the flow pattern classification and superficial velocity prediction tasks. The dynamic routing algorithm has also been improved to speed up model training. Extensive experiments validate the effectiveness of the improved modules. Finally, we compare the proposed network with its variants and other competing networks. The better performance results show that our multi-task sequence-based CapsNet has great potential for dealing with high-dimensional, time-varying and nonlinear problems in multiphase flow. •A soft measurement for oil–water flow based on capsule network is proposed.•The CapsNet solve oil–water two flow measurement task simultaneously.•Time-dependent signal is imaged into space-dependent format to train CapsNet.•The CapsNet shows better generalization capability than other networks.