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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Chenquan Hua College of Mechanical Engineering Nanjing University of Science and Technology Nanjing ChinaChangming Wang College of Mechanical Engineering Nanjing University of Science and Technology Nanjing ChinaYanfeng Geng College of Information & Control Engineering China University of Petroleum Dongying China

A novel noninvasive approach to the on-line flow regime identification for wet gas flow in a horizontally mounted pipeline is proposed in this paper. Research into the flow-induced vibration response for the wet gas flow with the conditions of pipe diameter 50mm,pressure from 0.25MPa to 0.35MPa,Lockhart-Martinelli parameter from 0.02 to 0.6,and gas Froude Number from 0.5 to 2.7,was conducted. The flow-induced vibration signals were measured by a vibration transducer installed by outside wall of pipe,and then the features from the vibration signals were extracted though wavelet package transform （WPT）. A radial basis function network （RBFN） classifier with Gaussian basis function and the extracted features as inputs was developed to identify the three typical flow regimes including stratified wavy flow,annular mist flow,and slug flow for wet gas flow. The results show that the method can identify flow patterns effectively and its identification accuracy arrives at above 89%.

关键词： flow regime identification wet gas flow flow- induced vibration wavelet package transform radial basis function network

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Noninvasive flow regime identification for Wet Gas flow Based on flow-induced Vibration

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Chinese Journal of Chemical Engineering 2010年第5期18卷 795-803页

作者：华陈权王昌明耿艳峰石天明 College of Information & Control Engineering China University of Petroleum Dongying 257061 China College of Mechanical Engineering Nanjing University of Science and Technology Nanjing 210094 China

A novel noninvasive approach, based on flow-induced vibration, to the online flow regime identification for wet gas flow in a horizontal pipeline is proposed. Research into the flow-induced vibration response for the wet gas flow was conducted under the conditions of pipe diameter 50 mm, pressure from 0.25 MPa to 0.35 MPa, Lockhart-Martinelli parameter from 0.02 to 0.6, and gas Froude Number from 0.5 to 2.7. The flow-induced vibration signals were measured by a transducer installed on outside wall of pipe, and then the normalized energy features from different frequency bands in the vibration signals were extracted through 4-scale wavelet package transform. A "binary tree" multi-class support vector machine(MCSVM) classifier, with the normalized feature vector as inputs, and Gaussian radial basis function as kernel function, was developed to identify the three typical flow regimes including stratified wavy flow, annular mist flow, and slug flow for wet gas flow. The results show that the method can identify effectively flow regimes and its identification accuracy is about 93.3%. Comparing with the other classifiers, the MCSVM classifier has higher accuracy, especially under the case of small samples. The noninvasive measurement approach has great application prospect in online flow regime identification.

关键词： flow regime identification wet gas flow flow-induced vibration wavelet package transform support vector machine

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Application of D-S Evidence Theory in flow regime identification of Two-Phase Horizontal Pipe flow

Application of D-S Evidence Theory in Flow Regime Identifica...

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第二十七届中国控制会议

作者： Zhang Fusheng,Dong Feng Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation,Tianjin University,Tianjin 300072,P.R.China

identification of two-phase flow regimes is deadly required in many industrial processes and is difficult to perform ***-sensor data fusion has been applied to the recognition of flow regimes of air/water flow in horizontal pipe.A specific fusion process architecture based on Dasarathy fusion model is introduced,and D-S evidence theory is adopted on the decision fusion *** fusion data is provided by dual-plane electrical resistance tomography system in which each plane works as a single ***-correlation technique is used to match the data from the two electrode *** is shown that this flow regime recognition method in which multi-sensor fusion is adopted is an approval strategy.

关键词： Two-Phase flow flow regime identification Electrical Resistance Tomography D-S evidence theory Cross-Correlation Technique

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identification Method of Gas-Liquid Two-phase flow regime Based on Image Multi-feature Fusion and Support Vector Machine

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Chinese Journal of Chemical Engineering 2008年第6期16卷 832-840页

作者：周云龙陈飞孙斌 School of Energyand Mechanical Engineering Northeast Dianli University Jilin 132012 China School of Automatic Engineering Northeast Dianli University Jilin 132012 China

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

关键词： flow regime identification gas-liquid two-phase flow image processing multi-feature fusion support vector machine

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Investigation of gas-solids flow hydrodynamics in a cold model of a dual fluidised bed gasifier using electrical capacitance tomography sensors

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Particuology 2020年第4期18卷 193-204页

作者： Zhan Luo Yonglei Lin Qiuya Tu Wuqiang Yang Haigang Wang Institute of Engineering Thermophysics Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical and Electronic Engineering University of ManchesterManchesterM139PLUK

The flow characteristics of a dual fluidised bed gasifier(DFBG)are more complex than those of a single fluidised bed *** stable operation and appropriate control,a cold DFBG test facility with both an upper and a lower U-valve was built,and electrical capacitance tomography(ECT)sensors were installed with pressure transducers to investigate the effects of operating conditions on gas-solids flow *** operating parameters included gas velocities in the riser and in the bubbling fluidised bed,aeration velocity in the lower U-valve,bed material inventory,and particle *** is the first time that ECT was applied in different flow zones of a dual fluidised bed gasifier *** experimental results indicated that ECT in the recycle chamber could monitor the performance of the lower U-valve under different operating conditions for early detection of gas shortcut from the riser to the bubbling *** main flow regimes in the riser and the differences between the reactors were identified by two sets of ECT sensors with pressure ***,the effects of the operating conditions on the pressure drop in different parts of the DFBG was investigated.

关键词： Dual fluidised bed gasifier Hydrodynamics Electrical capacitance tomography flow regime identification

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identification OF GAS LIQUID flow regimeS IN A HORIZONTAL flow USING NEURAL NETWORK

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Journal of Hydrodynamics 2005年第1期17卷 66-73页

作者： JIAZhi-hai NIUGang WANGJing SchoolofMechanicalandPowerEngineering.ShanghaiJiaotongUniversity.Shanghai200030.China

The knowledge of flow regimes is very important in the study of a two phase flow system. A new flow regime identification method based on a Probability Density Function (PDF) and a neural network is proposed in this paper. The instantaneous differential pressure signals of a horizontal flow were acquired with a differential pressure sensor. The characters of differential pressure signals for different flow regimes are analyzed with the PDF. Then, four characteristic parameters of the PDF curves are defined, the peak number (K 1 ), the maximum peak value (K 2 ), the peak position (K 3 ) and the PDF variance (K 4 ). The characteristic vectors which consist of the four characteristic parameters as the input vectors train the neural network to classify the flow regimes. Experimental results show that this novel method for identifying air water two phase flow regimes has the advantages with a high accuracy and a fast response. The results clearly demonstrate that this new method could provide an accurate identification of flow regimes.

关键词： flow regime identification Probability Density Function (PDF) neural network two-phase flow flow regime

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