A Deep Branch-Aggregation Network for Recognition of Gas–Liquid Two-Phase Flow Structure

Gao, Zhong-Ke; Qu, Zhiyong; Cai, Qing; Hou, Linhua; Liu, Ming-Xu; Yuan, Tao

doi:10.1109/tim.2020.3022188

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…Feng Nie et al [11] utilized CNN algorithms to classify flow patterns in images of methane and tetrafluoromethane within horizontal circular pipes, achieving a testing dataset accuracy exceeding 90.63% and an average accuracy surpassing 97.56% for all data points in the database. Zhongke Gao et al [12] designed a branch-aggregation network (BAN) for classifying flow patterns in gas-liquid two-phase flow images, achieving a fast convergence speed and a recognition accuracy of 99.60%, highlighting its advantage in noise resistance. Zhong-Ke Gao et al [13] proposed a deep learning method based on complex networks that combined the original signals of limited penetrable visibility graphs (LPVG) with images for flow pattern classification and gas void fraction measurement.…”

Section: Introductionmentioning

confidence: 99%

Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

Wang,

Huang,

et al. 2024

Applied Sciences

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Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.

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Section: Introductionmentioning

confidence: 99%

Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

Wang,

Huang,

et al. 2024

Applied Sciences

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“…[10][11][12][13] A novel deep learning network is utilized to recognize the flow structures for the different flow conditions, which is of great significance for the optimization of industrial processes. [14] Based on the image recognition technology, the state performance of industrial processes can be evaluated. [15][16][17] Based on a quantization-neural network, a novel performance evaluating method for a flotation process is investigated by statistical modelling.…”

Section: Introductionmentioning

confidence: 99%

State evaluation of copper flotation process based on transfer learning and a layered and blocked framework

Wang

Peng

et al. 2023

Can J Chem Eng

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State evaluation is vital to ensure the process operating optimality for copper flotation processes. Specifically, the froth image is the comprehensive embodiment of raw ore properties and process operations, which is one of the key factors to realize condition recognition and state evaluation. Firstly, a feature mosaic technique‐based neural network framework is proposed. The input image features are extracted from the different network structures, which can achieve higher precision in condition recognition and state evaluation than a single neural network framework. Then, an improved deep convolutional generative adversarial networks (DCGAN) model based on feature matching and maximize mean discrepancy (MMD) distance is investigated so that the froth images with high similarity, integrity, and balance to the original images can be generated. Therefore, the problem of small image sets and the lack of labelled images for some sub‐processes can be solved. Finally, a layered and blocked state evaluation model is constructed based on the improved DCGAN model and transfer learning (TL) so that the state evaluation of the copper flotation process with multiple sub‐processes, long process, and small image sets of some sub‐processes is solved. The effectiveness of the proposed method is verified through a series of data experiments on a copper flotation industrial process.

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“…In recent years, soft computing techniques have been widely applied to facilitate the characterization of multiphase flow by establishing the relationship with the variables that can be measured directly [8]. For example, convolutional neural networks were developed for patterns identification of multiphase flows [9], [10], [11]. Artificial neural network (ANN) and support vector machine (SVM) were trained with experimental data to measure the mass flow rate and the fraction of individual phase in gas-solid or gas-liquid twophase flows [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Measurement of cross-sectional velocity distribution of pneumatically conveyed particles in a square-shaped pipe through electrostatic sensing and Gaussian process regression

Wang

Qian

et al. 2022

2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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A Deep Branch-Aggregation Network for Recognition of Gas–Liquid Two-Phase Flow Structure

Cited by 11 publications

References 26 publications

Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

State evaluation of copper flotation process based on transfer learning and a layered and blocked framework

Measurement of cross-sectional velocity distribution of pneumatically conveyed particles in a square-shaped pipe through electrostatic sensing and Gaussian process regression

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