IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p12875-d1225268.html
   My bibliography  Save this article

An Application of Ultrasonic Waves in the Pretreatment of Biological Sludge in Urban Sewage and Proposing an Artificial Neural Network Predictive Model of Concentration

Author

Listed:
  • Atef El Jery

    (Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Houman Kosarirad

    (Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln, 122 NH, Lincoln, NE 68588, USA)

  • Nedasadat Taheri

    (School of Computing, University of Nebraska–Lincoln, Lincoln, NE 68588, USA)

  • Maryam Bagheri

    (Department of Mechanical Engineering, University of Houston, Houston, TX 77004, USA)

  • Moutaz Aldrdery

    (Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Abubakr Elkhaleefa

    (Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Chongqing Wang

    (School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Saad Sh. Sammen

    (Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 10047, Iraq)

Abstract

This research examines whether ultrasonic waves can enhance the hydrolysis, stability, and dewatering of activated sludge from raw urban wastewater. Sampling and physical examination of the activated sludge that was returned to the aeration pond were carried out using ultrasonic waves that were guided at frequencies of 30 and 50 kHz for periods of 0.5, 1, 3, 5, 10, 15, and 30 min. Various tests, including volatile suspended solids, inorganic solids, volatile solids, sludge resistant time, capillary suction time, total suspended solids, total solids, and volatile soluble solids, were carried out to advance further the processes of hydrolysis, stabilization, and dehydration of samples. According to the observations, the volatile soluble solids at a frequency of 30 kHz and t = 15 min were raised by 72%. The capillary suction time of 30 and 50 kHz in 1 min demonstrated a drop of 29 and 22%, respectively. It is crucial to consider that, at 10 min and the frequency of 50 kHz, the greatest efficiency was found. The 30 kHz and 1 min yielded the optimum sludge dewatering conditions. Finally, artificial neural networks (ANN) are utilized to propose predictive models for concentration, and the results were also very accurate ( M A E = 1.37 % ). Regarding the computational costs, the ANN took approximately 5% of the time spent on experiments.

Suggested Citation

  • Atef El Jery & Houman Kosarirad & Nedasadat Taheri & Maryam Bagheri & Moutaz Aldrdery & Abubakr Elkhaleefa & Chongqing Wang & Saad Sh. Sammen, 2023. "An Application of Ultrasonic Waves in the Pretreatment of Biological Sludge in Urban Sewage and Proposing an Artificial Neural Network Predictive Model of Concentration," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12875-:d:1225268
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/12875/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/12875/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhenxing Wang & Yunjun Yu & Kallol Roy & Cheng Gao & Lei Huang, 2023. "The Application of Machine Learning: Controlling the Preparation of Environmental Materials and Carbon Neutrality," IJERPH, MDPI, vol. 20(3), pages 1-4, January.
    Full references (including those not matched with items on IDEAS)

    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.

      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:gam:jsusta:v:15:y:2023:i:17:p:12875-:d:1225268. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.