Detecting the presence of large biomass particles in pneumatic conveying pipelines using an acoustic sensor

  • Duo Sun
  • , Yong Yan
  • , Robert M. Carter
  • , Gang Lu
  • , Gerry Riley
  • , Matthew Wood

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a novel approach to online automatic detection of the presence of large biomass particles in a pneumatic conveying pipeline using an acoustic emission sensor and time-frequency analysis techniques. The acoustic sensor is used to capture the sound emitted from the collisions between biomass particles and pipe wall. Time-frequency analysis technique is used to eliminate environmental noise from the acoustic signal, extract the revealing information about the collisions, and identify the large particles. The acoustic sensor together with its signal conditioning unit is integrated into a compact enclosure, which can be easily attached to the outer face of a pneumatic pipeline. Experimental results obtained from an industrial pneumatic conveyor demonstrate the method works well and results are promising.

Original languageEnglish
Title of host publication2013 IEEE International Instrumentation and Measurement Technology Conference
Subtitle of host publicationInstrumentation and Measurement for Life, I2MTC 2013 - Proceedings
Pages1487-1490
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement for Life, I2MTC 2013 - Minneapolis, MN, United States
Duration: 6 May 20139 May 2013

Publication series

NameConference Record - IEEE Instrumentation and Measurement Technology Conference
ISSN (Print)1091-5281

Conference

Conference2013 IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement for Life, I2MTC 2013
Country/TerritoryUnited States
CityMinneapolis, MN
Period6/05/139/05/13

Keywords

  • acoustic sensor
  • biomass
  • fuel handling
  • large particle detection
  • time-frequency analysis

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