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

Lei OuYang; Ningde Jin; Landi Bai; Weikai Ren

doi:10.1016/j.isatra.2022.12.007

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

ISA Trans. 2023 Jun:137:629-645. doi: 10.1016/j.isatra.2022.12.007. Epub 2022 Dec 13.

Authors

Lei OuYang¹, Ningde Jin², Landi Bai³, Weikai Ren⁴

Affiliations

¹ School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People's Republic of China. Electronic address: 1018234032@tju.edu.cn.
² School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People's Republic of China. Electronic address: ndjin@tju.edu.cn.
³ School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People's Republic of China. Electronic address: bld_27@tju.edu.cn.
⁴ School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People's Republic of China. Electronic address: renweikai1222@tju.edu.cn.

PMID: 36572629
DOI: 10.1016/j.isatra.2022.12.007

Abstract

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.

Keywords: Capsule network; Multi-task learning; Oil–water flow; Soft measurement; Superficial velocity prediction.