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Use of acoustic emission in combination with machine learning: monitoring of gas–liquid mixing in stirred tanks

Author

Listed:
  • Giuseppe Forte

    (Johnson Matthey Technology Centre
    University of Birmingham)

  • Federico Alberini

    (University of Birmingham)

  • Mark Simmons

    (University of Birmingham)

  • Hugh E. Stitt

    (Johnson Matthey Technology Centre)

Abstract

Operations involving gas–liquid agitated vessels are common in the biochemical and chemical industry; ensuring good contact between the two phases is essential to process performance. In this work, a methodology to compute acoustic emission data, recorded using a piezoelectric sensor, to evaluate the gas–liquid mixing regime within gas–liquid and gas–solid–liquid mixtures was developed. The system was a 3L stirred tank equipped with a Rushton Turbine and a ring sparger. Whilst moving up through the vessel, gas bubbles collapse, break or coalesce generating sound waves transmitted through the wall to the acoustic transmitter. The system was operated in different flow regimes (non-gassed condition, loaded and complete dispersion) achieved by varying impeller speed and gas flow rate, with the objective to feed machine learning algorithms with the acoustic spectrum to univocally identify the different conditions. The developed method allowed to successfully recognise the operating regime with an accuracy higher than 90% both in absence and presence of suspended particles.

Suggested Citation

  • Giuseppe Forte & Federico Alberini & Mark Simmons & Hugh E. Stitt, 2021. "Use of acoustic emission in combination with machine learning: monitoring of gas–liquid mixing in stirred tanks," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 633-647, February.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01611-z
    DOI: 10.1007/s10845-020-01611-z
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    References listed on IDEAS

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    1. Dezhen Xue & Prasanna V. Balachandran & John Hogden & James Theiler & Deqing Xue & Turab Lookman, 2016. "Accelerated search for materials with targeted properties by adaptive design," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
    2. James Griffin & Xun Chen, 2016. "Real-time simulation of neural network classifications from characteristics emitted by acoustic emission during horizontal single grit scratch tests," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 507-523, June.
    3. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
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    Cited by:

    1. Weinan Liu & Guojun Zhang & Yu Huang & Wenyuan Li & Youmin Rong & Ranwu Yang, 2023. "A novel monitoring method of nanosecond laser scribing float glass with acoustic emission," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1721-1729, April.

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