IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8314-d857612.html
   My bibliography  Save this article

Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality

Author

Listed:
  • Huixian Shi

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Zijing Wang

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Haiyi Zhou

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Kaiyan Lin

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Shuping Li

    (College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China)

  • Xinnan Zheng

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Zheng Shen

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China
    Shanghai Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Jiaoliao Chen

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Lei Zhang

    (College of Transportation Engineering, Tongji University, Shanghai 200092, China)

  • Yalei Zhang

    (College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China)

Abstract

The treatment of wastewater is a complicated biological reaction process. Reliable effluent prediction is critical in the scientific management of water treatment plants. This research proposes a soft sensor design strategy to address the issues above, Multi-Verse Optimizer (MVO)-based random vector functional link network (MVO-RVFL). The proposed approach is utilized to anticipate real-time effluent data obtained from the Benchmark Simulation Model 1 (BSM1). The results of the experiments demonstrate that the MVO methodology can successfully find the optimum input-hidden weights and hidden biases of the RVFL model while outperforming the original RVFL and other typical machine learning approaches in all types of influent datasets. In the situation of significant water quality variations, the use of the fusion process for model development was also investigated. The experimental results demonstrate that incorporating prior knowledge can effectively improve the model’s ability to cope with unexpected situations.

Suggested Citation

  • Huixian Shi & Zijing Wang & Haiyi Zhou & Kaiyan Lin & Shuping Li & Xinnan Zheng & Zheng Shen & Jiaoliao Chen & Lei Zhang & Yalei Zhang, 2022. "Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8314-:d:857612
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8314/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/14/8314/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    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.
    1. Jun Yuan & Jiang Zhu & Victor Nian, 2020. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures," Sustainability, MDPI, vol. 12(24), pages 1-14, December.
    2. Shivam Pandey & Bhekisipho Twala & Rajesh Singh & Anita Gehlot & Aman Singh & Elisabeth Caro Montero & Neeraj Priyadarshi, 2022. "Wastewater Treatment with Technical Intervention Inclination towards Smart Cities," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    3. Golzar, Farzin & Silveira, Semida, 2021. "Impact of wastewater heat recovery in buildings on the performance of centralized energy recovery – A case study of Stockholm," Applied Energy, Elsevier, vol. 297(C).
    4. Basma Souayeh & Suvanjan Bhattacharyya & Najib Hdhiri & Mir Waqas Alam, 2021. "Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape," Sustainability, MDPI, vol. 13(6), pages 1-24, March.
    5. Sabina Kordana-Obuch & Mariusz Starzec & Daniel Słyś, 2021. "Assessment of the Feasibility of Implementing Shower Heat Exchangers in Residential Buildings Based on Users’ Energy Saving Preferences," Energies, MDPI, vol. 14(17), pages 1-30, September.
    6. Franz Huber & Georg Neugebauer & Thomas Ertl & Florian Kretschmer, 2020. "Suitability Pre-Assessment of in-Sewer Heat Recovery Sites Combining Energy and Wastewater Perspectives," Energies, MDPI, vol. 13(24), pages 1-32, December.
    7. Nilsson, David & Karpouzoglou, Timos & Wallin, Jörgen & Blomkvist, Pär & Golzar, Farzin & Martin, Viktoria, 2023. "Is on-property heat and greywater recovery a sustainable option? A quantitative and qualitative assessment up to 2050," Energy Policy, Elsevier, vol. 182(C).
    8. Liu, Qipeng & Li, Ran & Dereli, Recep Kaan & Flynn, Damian & Casey, Eoin, 2022. "Water resource recovery facilities as potential energy generation units and their dynamic economic dispatch," Applied Energy, Elsevier, vol. 318(C).

    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:14:y:2022:i:14:p:8314-:d:857612. 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.