IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i2d10.1007_s10845-020-01611-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01611-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01611-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    2. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    3. Suarez, Javier & Sánchez Serrano, Antonio, 2018. "Approaching non-performing loans from a macroprudential angle," Report of the Advisory Scientific Committee 7, European Systemic Risk Board.
    4. José Eduardo Gómez González & Ines Paola Orozco Hinojosa, 2010. "Un modelo de alerta temprana para el sistema financiero colombiano," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 28(62), pages 124-147, June.
    5. Philip Swicegood & Jeffrey A. Clark, 2001. "Off‐site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(3), pages 169-186, September.
    6. Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 325-342, January.
    7. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    8. Michael Halling & Evelyn Hayden, 2008. "Bank failure prediction: a two-step survival time approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The IFC's contribution to the 56th ISI Session, Lisbon, August 2007, volume 28, pages 48-73, Bank for International Settlements.
    9. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    10. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
    11. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    12. Daniel Porath, 2006. "Estimating probabilities of default for German savings banks and credit cooperatives," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(3), pages 214-233, July.
    13. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    14. Fatima Alali & Silvia Romero, 2013. "Characteristics of failed U.S. commercial banks: an exploratory study," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(4), pages 1149-1174, December.
    15. Paola Bongini & Stijn Claessens & Giovanni Ferri, 2001. "The Political Economy of Distress in East Asian Financial Institutions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 19(1), pages 5-25, February.
    16. Allen N. Berger & Björn Imbierowicz & Christian Rauch, 2016. "The Roles of Corporate Governance in Bank Failures during the Recent Financial Crisis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 729-770, June.
    17. Nijskens, Rob & Mokas, Dimitris, 2019. "Credit Risk in Commercial Real Estate Bank Loans : The Role of Idiosyncratic versus Macro-Economic Factors," Other publications TiSEM ea4f2f0e-dc50-4987-91d3-6, Tilburg University, School of Economics and Management.
    18. Paramonovs Sergejs & Ijevleva Ksenija, 2015. "The Role of Marketing Tools in the Improvement of Consumers Financial Literacy," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 27(1), pages 40-45, December.
    19. repec:hum:wpaper:sfb649dp2013-037 is not listed on IDEAS
    20. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    21. Zhiyong Li & Xinyi Hu & Ke Li & Fanyin Zhou & Feng Shen, 2020. "Inferring the outcomes of rejected loans: an application of semisupervised clustering," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 631-654, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01611-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.