Study on prediction model of liquid holdup based on back propagation neural network optimized by tuna swarm algorithm-Reference-Cited by-同舟云学术

Study on prediction model of liquid holdup based on back propagation neural network optimized by tuna swarm algorithm

Published:2023-06-27 Issue:3 Volume:45 Page:8623-8641
ISSN:1556-7036
Container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
language:en
Short-container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects

Author:

Xiao Rongge¹^ORCID,Liu Guoqing¹,Yi Dongrui²,Liu Bo¹,Zhuang Qi³^ORCID

Affiliation:

1. College of Petroleum Engineering and Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil & Gas Reservoirs, Xi’an Shiyou University, Xi’an, Shaanxi, China

2. Surface Engineering Department, Research Institute of Shaanxi Yanchang Petroleum (Group) Company Ltd, Xi’an, Shaanxi, China

3. The Second Gas Production Plant, PetroChina Changqing Oilfield Company, Xi’an, Shaanxi, China

Publisher

Informa UK Limited

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15567036.2023.2229269

Reference35 articles.

1. Liquid holdup in horizontal two-phase gas—liquid flow

2. Andritsos, N. 1986. Effect of pipe diameter and liquid viscosity on horizontal stratified flow (two-phase, wave generation). University of Illinois at Urbana-Champaign.

3. Pressure gradient and holdup in horizontal two-phase gas–liquid flows with low liquid loading

4. A new mechanistic model to predict gas-liquid interface shape of gas-liquid flow through pipes with low liquid loading

5. A comprehensive comparison of the performance of several popular models to predict pressure drop in stratified gas–liquid flow with low liquid loading

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