The Experts below are selected from a list of 2652 Experts worldwide ranked by ideXlab platform

Ashutosh Sharma - One of the best experts on this subject based on the ideXlab platform.

  • a novel local singularity distribution based method for Flow Regime Identification gas liquid stirred vessel with rushton turbine
    Chemical Engineering Science, 2006
    Co-Authors: A M Jade, Valadi K Jayaraman, Avinash R. Khopkar, B D Kulkarni, Vivek V. Ranade, Ashutosh Sharma
    Abstract:

    A novel method employing a unique combination of wavelet based local singularity analysis and support vector machines (SVM) classification is described and illustrated by considering the case example of Flow Regime Identification in gas-liquid stirred tank equipped with Rushton turbine. Pressure fluctuations time series data obtained at different operating conditions were first analyzed to obtain the distribution of local Holder exponents' estimates. The relevant features from this distribution were then used as input data to the SVM classifier. Employing this method we could classify Flow Regimes with 98% accuracy. The results highlight the fact that the local scaling behavior of a given Regime follows a distinct pattern. Further, the singularity features can be employed by intelligent machine learning based algorithms like SVM for successful online Regime Identification. The method can be readily applied to the other multiphase systems like bubble column, fluidized bed, etc.

  • A novel local singularity distribution based method for Flow Regime Identification: Gas¿liquid stirred vessel with Rushton turbine
    Chemical Engineering Science, 2006
    Co-Authors: A M Jade, Valadi K Jayaraman, Avinash R. Khopkar, B D Kulkarni, Vivek V. Ranade, Ashutosh Sharma
    Abstract:

    A novel method employing a unique combination of wavelet based local singularity analysis and support vector machines (SVM) classification is described and illustrated by considering the case example of Flow Regime Identification in gas-liquid stirred tank equipped with Rushton turbine. Pressure fluctuations time series data obtained at different operating conditions were first analyzed to obtain the distribution of local Holder exponents' estimates. The relevant features from this distribution were then used as input data to the SVM classifier. Employing this method we could classify Flow Regimes with 98% accuracy. The results highlight the fact that the local scaling behavior of a given Regime follows a distinct pattern. Further, the singularity features can be employed by intelligent machine learning based algorithms like SVM for successful online Regime Identification. The method can be readily applied to the other multiphase systems like bubble column, fluidized bed, etc.

Jingqi Yuan - One of the best experts on this subject based on the ideXlab platform.

  • Time–Frequency Analysis Based Flow Regime Identification Methods for Airlift Reactors
    Industrial & Engineering Chemistry Research, 2012
    Co-Authors: Lijia Luo, Ying Yan, Jingqi Yuan
    Abstract:

    The Flow Regime transitions in an airlift reactor were investigated based on pressure fluctuation signals. Two time–frequency analysis methods, i.e., Wigner–Ville distribution and wavelet transform, were used to extract Flow Regime characteristics from pressure signals. The main frequency derived from the smoothed pseudo-Wigner–Ville distribution of the pressure signal was used to quantify Flow Regime transitions in the reactor. Two Flow Regime transition points were successfully detected from the evolution of main frequencies of pressure signals. In addition, the local dynamic characteristics of the pressure signal at different frequency bands were analyzed by use of the wavelet transform. A new Flow Regime Identification method based on the wavelet entropy of the pressure signal was proposed. This method was confirmed to be reliable and efficient to detect Flow Regime transitions in the reactor.

  • time frequency analysis based Flow Regime Identification methods for airlift reactors
    Industrial & Engineering Chemistry Research, 2012
    Co-Authors: Lijia Luo, Ying Yan, Jingqi Yuan
    Abstract:

    The Flow Regime transitions in an airlift reactor were investigated based on pressure fluctuation signals. Two time–frequency analysis methods, i.e., Wigner–Ville distribution and wavelet transform, were used to extract Flow Regime characteristics from pressure signals. The main frequency derived from the smoothed pseudo-Wigner–Ville distribution of the pressure signal was used to quantify Flow Regime transitions in the reactor. Two Flow Regime transition points were successfully detected from the evolution of main frequencies of pressure signals. In addition, the local dynamic characteristics of the pressure signal at different frequency bands were analyzed by use of the wavelet transform. A new Flow Regime Identification method based on the wavelet entropy of the pressure signal was proposed. This method was confirmed to be reliable and efficient to detect Flow Regime transitions in the reactor.

A M Jade - One of the best experts on this subject based on the ideXlab platform.

  • a novel local singularity distribution based method for Flow Regime Identification gas liquid stirred vessel with rushton turbine
    Chemical Engineering Science, 2006
    Co-Authors: A M Jade, Valadi K Jayaraman, Avinash R. Khopkar, B D Kulkarni, Vivek V. Ranade, Ashutosh Sharma
    Abstract:

    A novel method employing a unique combination of wavelet based local singularity analysis and support vector machines (SVM) classification is described and illustrated by considering the case example of Flow Regime Identification in gas-liquid stirred tank equipped with Rushton turbine. Pressure fluctuations time series data obtained at different operating conditions were first analyzed to obtain the distribution of local Holder exponents' estimates. The relevant features from this distribution were then used as input data to the SVM classifier. Employing this method we could classify Flow Regimes with 98% accuracy. The results highlight the fact that the local scaling behavior of a given Regime follows a distinct pattern. Further, the singularity features can be employed by intelligent machine learning based algorithms like SVM for successful online Regime Identification. The method can be readily applied to the other multiphase systems like bubble column, fluidized bed, etc.

  • A novel local singularity distribution based method for Flow Regime Identification: Gas¿liquid stirred vessel with Rushton turbine
    Chemical Engineering Science, 2006
    Co-Authors: A M Jade, Valadi K Jayaraman, Avinash R. Khopkar, B D Kulkarni, Vivek V. Ranade, Ashutosh Sharma
    Abstract:

    A novel method employing a unique combination of wavelet based local singularity analysis and support vector machines (SVM) classification is described and illustrated by considering the case example of Flow Regime Identification in gas-liquid stirred tank equipped with Rushton turbine. Pressure fluctuations time series data obtained at different operating conditions were first analyzed to obtain the distribution of local Holder exponents' estimates. The relevant features from this distribution were then used as input data to the SVM classifier. Employing this method we could classify Flow Regimes with 98% accuracy. The results highlight the fact that the local scaling behavior of a given Regime follows a distinct pattern. Further, the singularity features can be employed by intelligent machine learning based algorithms like SVM for successful online Regime Identification. The method can be readily applied to the other multiphase systems like bubble column, fluidized bed, etc.

Lijia Luo - One of the best experts on this subject based on the ideXlab platform.

  • Time–Frequency Analysis Based Flow Regime Identification Methods for Airlift Reactors
    Industrial & Engineering Chemistry Research, 2012
    Co-Authors: Lijia Luo, Ying Yan, Jingqi Yuan
    Abstract:

    The Flow Regime transitions in an airlift reactor were investigated based on pressure fluctuation signals. Two time–frequency analysis methods, i.e., Wigner–Ville distribution and wavelet transform, were used to extract Flow Regime characteristics from pressure signals. The main frequency derived from the smoothed pseudo-Wigner–Ville distribution of the pressure signal was used to quantify Flow Regime transitions in the reactor. Two Flow Regime transition points were successfully detected from the evolution of main frequencies of pressure signals. In addition, the local dynamic characteristics of the pressure signal at different frequency bands were analyzed by use of the wavelet transform. A new Flow Regime Identification method based on the wavelet entropy of the pressure signal was proposed. This method was confirmed to be reliable and efficient to detect Flow Regime transitions in the reactor.

  • time frequency analysis based Flow Regime Identification methods for airlift reactors
    Industrial & Engineering Chemistry Research, 2012
    Co-Authors: Lijia Luo, Ying Yan, Jingqi Yuan
    Abstract:

    The Flow Regime transitions in an airlift reactor were investigated based on pressure fluctuation signals. Two time–frequency analysis methods, i.e., Wigner–Ville distribution and wavelet transform, were used to extract Flow Regime characteristics from pressure signals. The main frequency derived from the smoothed pseudo-Wigner–Ville distribution of the pressure signal was used to quantify Flow Regime transitions in the reactor. Two Flow Regime transition points were successfully detected from the evolution of main frequencies of pressure signals. In addition, the local dynamic characteristics of the pressure signal at different frequency bands were analyzed by use of the wavelet transform. A new Flow Regime Identification method based on the wavelet entropy of the pressure signal was proposed. This method was confirmed to be reliable and efficient to detect Flow Regime transitions in the reactor.

Yan Feng Geng - One of the best experts on this subject based on the ideXlab platform.

  • Flow Regime Identification for Wet Gas Flow Based on Kurtosis Feature of Flow-Induced Pipeline Vibration
    Applied Mechanics and Materials, 2014
    Co-Authors: Chen Quan Hua, Yan Feng Geng, Hua Wei Pan, Lan Chang Xing
    Abstract:

    Flow-induced vibration occurs widely in Flow pipelines, and pipeline vibration signals have various frequency characteristics corresponding to different Flow Regimes. Therefore, an novel noninvasive approach to Flow Regime Identification for wet gas Flow in a horizontal pipeline is presented in this paper. The vibration signals were collected by a transducer installed on external wall of pipeline. Empirical mode decomposition (EMD) was used to decompose the vibration signal into different intrinsic mode functions (IMFs), and then the kurtosis of each IMF component for each experimental data was calculated. Finally, the IMF kurtosis feature vector was input to the support vector machine (SVM) to identify three typical Flow Regimes for wet gas Flow including stratified/stratified wavy Flow, annular/annular mist Flow and slug Flow in a horizontal pipeline 50 mm in diameter. The experimental results show that the proposed approach can identify Flow Regimes effectively, and the Identification rate is 80.6%.

  • Wet Gas Flow Regime Identification Based on WPT and PNN
    Applied Mechanics and Materials, 2011
    Co-Authors: Chen Quan Hua, Yan Feng Geng
    Abstract:

    A novel noninvasive approach 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, with 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 vibration transducer installed by 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 (WPT) with mother wavelet db7. A probabilistic neural network (PNN) classifier with 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 effectively Flow Regimes and its Identification accuracy arrives at above 92.1%. The noninvasive measurement approach has great application prospect in online Flow Regime Identification.

  • Noninvasive Flow Regime Identification for Wet Gas Flow Based on Flow-induced Vibration
    Chinese Journal of Chemical Engineering, 2010
    Co-Authors: Chen Quan Hua, Yan Feng Geng, Changming Wang, Tianming Shi
    Abstract:

    Abstract 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.

  • Flow Regime Identification for wet gas Flow based on WPT and RBFN
    2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009
    Co-Authors: Chen Quan Hua, Changming Wang, Yan Feng Geng
    Abstract:

    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%.