Acoustic signal processing with robust machine learning algorithm for improved monitoring of particulate solid materials in a gas pipeline

Kuda Tijjani Aminu, Don McGlinchey, Andrew Cowell

Research output: Contribution to journalArticle

Abstract

The flow of particulate solid materials in a gas flowline can significantly erode mechanical equipment. Hence, real-time quantitative monitoring is a timely need for the oil and gas industry to achieve real-time control and production optimisation. Although a considerable amount of research has been conducted employing acoustic signals for qualitative monitoring, there is still an unmet demand for a simple and robust real-time quantitative monitoring system. Acoustic signal processing with machine learning is a simple and robust method that has the potential to meet this demand but has not been previously exploited for real-time quantitative monitoring of particulate solid materials in a gas flowline. This paper proposes a novel instrumentation system for on-line measurement of solid flow rate, solid concentration, line pressure drop and gas velocity in a gas-solid multiphase flow using acoustic sensing technology coupled with signal processing techniques and machine learning algorithm. The acoustic sensor is used to capture the acoustic wave emitted from the impingements of the solid particles on the bend component of the flowline. Signal processing techniques are used to extract relevant features about the impingements. An integrated, conventional Artificial Neural Network (ANN) is used to capture the distribution of the acoustic feature vectors in order to establish the relationship between the measurands and the acoustic signal. However, conventional ANNs are mainly concerned with capturing systematic patterns in a distribution of measurements fixed in time and in this case the dynamics of the generated acoustic signal varies with time. A modification, called Time-Delay Neural Network (TDNN) is used to capture such dynamics. The proposed system compares the performance of the classical ANN and the TDNN models. Results obtained demonstrate that with the classical ANN, the normalised root mean square error (NRMSE) is 0.66, 0.29, 0.26 and 0.46 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. With the TDNN model, the NRMSE is 0.18, 0.17, 0.20 and 0.16 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. In comparison with the ANN model, the TDNN model has better performance as the NRMSE values are lower for all the models for the measurands. Overall, this study lays the basis for employing signal processing techniques and machine learning algorithm in the development of a simple, reliable and low cost real-time quantitative particulate solid flow monitoring system.
Original languageEnglish
Pages (from-to)33-44
Number of pages12
JournalFlow Measurement and Instrumentation
Volume65
Early online date15 Nov 2018
DOIs
Publication statusPublished - Mar 2019

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Acoustic signal processing
machine learning
Gas pipelines
Learning algorithms
particulates
Learning systems
Signal Processing
signal processing
Learning Algorithm
Machine Learning
Acoustics
Monitoring
solids flow
Neural networks
Flow of solids
acoustics
Flowlines
gases
root-mean-square errors
Time delay

Keywords

  • acoustic signal processing
  • Grey Wolf Optimiser (GWO)
  • ANN
  • TDNN
  • condition monitoring

Cite this

@article{01ef85d4fa534f4a8a99e07de0f55d7a,
title = "Acoustic signal processing with robust machine learning algorithm for improved monitoring of particulate solid materials in a gas pipeline",
abstract = "The flow of particulate solid materials in a gas flowline can significantly erode mechanical equipment. Hence, real-time quantitative monitoring is a timely need for the oil and gas industry to achieve real-time control and production optimisation. Although a considerable amount of research has been conducted employing acoustic signals for qualitative monitoring, there is still an unmet demand for a simple and robust real-time quantitative monitoring system. Acoustic signal processing with machine learning is a simple and robust method that has the potential to meet this demand but has not been previously exploited for real-time quantitative monitoring of particulate solid materials in a gas flowline. This paper proposes a novel instrumentation system for on-line measurement of solid flow rate, solid concentration, line pressure drop and gas velocity in a gas-solid multiphase flow using acoustic sensing technology coupled with signal processing techniques and machine learning algorithm. The acoustic sensor is used to capture the acoustic wave emitted from the impingements of the solid particles on the bend component of the flowline. Signal processing techniques are used to extract relevant features about the impingements. An integrated, conventional Artificial Neural Network (ANN) is used to capture the distribution of the acoustic feature vectors in order to establish the relationship between the measurands and the acoustic signal. However, conventional ANNs are mainly concerned with capturing systematic patterns in a distribution of measurements fixed in time and in this case the dynamics of the generated acoustic signal varies with time. A modification, called Time-Delay Neural Network (TDNN) is used to capture such dynamics. The proposed system compares the performance of the classical ANN and the TDNN models. Results obtained demonstrate that with the classical ANN, the normalised root mean square error (NRMSE) is 0.66, 0.29, 0.26 and 0.46 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. With the TDNN model, the NRMSE is 0.18, 0.17, 0.20 and 0.16 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. In comparison with the ANN model, the TDNN model has better performance as the NRMSE values are lower for all the models for the measurands. Overall, this study lays the basis for employing signal processing techniques and machine learning algorithm in the development of a simple, reliable and low cost real-time quantitative particulate solid flow monitoring system.",
keywords = "acoustic signal processing, Grey Wolf Optimiser (GWO), ANN, TDNN, condition monitoring",
author = "Aminu, {Kuda Tijjani} and Don McGlinchey and Andrew Cowell",
note = "Acceptance from webpage Published version title: {"}Acoustic signal processing with robust machine learning algorithm for improved monitoring of particulate solid materials in a gas ¿owline{"} AAM: 12m embargo",
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issn = "0955-5986",
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TY - JOUR

T1 - Acoustic signal processing with robust machine learning algorithm for improved monitoring of particulate solid materials in a gas pipeline

AU - Aminu, Kuda Tijjani

AU - McGlinchey, Don

AU - Cowell, Andrew

N1 - Acceptance from webpage Published version title: "Acoustic signal processing with robust machine learning algorithm for improved monitoring of particulate solid materials in a gas ¿owline" AAM: 12m embargo

PY - 2019/3

Y1 - 2019/3

N2 - The flow of particulate solid materials in a gas flowline can significantly erode mechanical equipment. Hence, real-time quantitative monitoring is a timely need for the oil and gas industry to achieve real-time control and production optimisation. Although a considerable amount of research has been conducted employing acoustic signals for qualitative monitoring, there is still an unmet demand for a simple and robust real-time quantitative monitoring system. Acoustic signal processing with machine learning is a simple and robust method that has the potential to meet this demand but has not been previously exploited for real-time quantitative monitoring of particulate solid materials in a gas flowline. This paper proposes a novel instrumentation system for on-line measurement of solid flow rate, solid concentration, line pressure drop and gas velocity in a gas-solid multiphase flow using acoustic sensing technology coupled with signal processing techniques and machine learning algorithm. The acoustic sensor is used to capture the acoustic wave emitted from the impingements of the solid particles on the bend component of the flowline. Signal processing techniques are used to extract relevant features about the impingements. An integrated, conventional Artificial Neural Network (ANN) is used to capture the distribution of the acoustic feature vectors in order to establish the relationship between the measurands and the acoustic signal. However, conventional ANNs are mainly concerned with capturing systematic patterns in a distribution of measurements fixed in time and in this case the dynamics of the generated acoustic signal varies with time. A modification, called Time-Delay Neural Network (TDNN) is used to capture such dynamics. The proposed system compares the performance of the classical ANN and the TDNN models. Results obtained demonstrate that with the classical ANN, the normalised root mean square error (NRMSE) is 0.66, 0.29, 0.26 and 0.46 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. With the TDNN model, the NRMSE is 0.18, 0.17, 0.20 and 0.16 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. In comparison with the ANN model, the TDNN model has better performance as the NRMSE values are lower for all the models for the measurands. Overall, this study lays the basis for employing signal processing techniques and machine learning algorithm in the development of a simple, reliable and low cost real-time quantitative particulate solid flow monitoring system.

AB - The flow of particulate solid materials in a gas flowline can significantly erode mechanical equipment. Hence, real-time quantitative monitoring is a timely need for the oil and gas industry to achieve real-time control and production optimisation. Although a considerable amount of research has been conducted employing acoustic signals for qualitative monitoring, there is still an unmet demand for a simple and robust real-time quantitative monitoring system. Acoustic signal processing with machine learning is a simple and robust method that has the potential to meet this demand but has not been previously exploited for real-time quantitative monitoring of particulate solid materials in a gas flowline. This paper proposes a novel instrumentation system for on-line measurement of solid flow rate, solid concentration, line pressure drop and gas velocity in a gas-solid multiphase flow using acoustic sensing technology coupled with signal processing techniques and machine learning algorithm. The acoustic sensor is used to capture the acoustic wave emitted from the impingements of the solid particles on the bend component of the flowline. Signal processing techniques are used to extract relevant features about the impingements. An integrated, conventional Artificial Neural Network (ANN) is used to capture the distribution of the acoustic feature vectors in order to establish the relationship between the measurands and the acoustic signal. However, conventional ANNs are mainly concerned with capturing systematic patterns in a distribution of measurements fixed in time and in this case the dynamics of the generated acoustic signal varies with time. A modification, called Time-Delay Neural Network (TDNN) is used to capture such dynamics. The proposed system compares the performance of the classical ANN and the TDNN models. Results obtained demonstrate that with the classical ANN, the normalised root mean square error (NRMSE) is 0.66, 0.29, 0.26 and 0.46 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. With the TDNN model, the NRMSE is 0.18, 0.17, 0.20 and 0.16 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. In comparison with the ANN model, the TDNN model has better performance as the NRMSE values are lower for all the models for the measurands. Overall, this study lays the basis for employing signal processing techniques and machine learning algorithm in the development of a simple, reliable and low cost real-time quantitative particulate solid flow monitoring system.

KW - acoustic signal processing

KW - Grey Wolf Optimiser (GWO)

KW - ANN

KW - TDNN

KW - condition monitoring

U2 - 10.1016/j.flowmeasinst.2018.11.015

DO - 10.1016/j.flowmeasinst.2018.11.015

M3 - Article

VL - 65

SP - 33

EP - 44

JO - Flow Measurement and Instrumentation

JF - Flow Measurement and Instrumentation

SN - 0955-5986

ER -