• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    A data driven.pdf
    Size:
    17.92Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Article
    Authors
    Harrou, Fouzi cc
    Cheng, Tuoyuan cc
    Sun, Ying cc
    Leiknes, TorOve cc
    Ghaffour, NorEddine cc
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Science and Engineering
    Environmental Science and Engineering Program
    Environmental Statistics Group
    Statistics Program
    Water Desalination and Reuse Research Center (WDRC)
    Date
    2020-10-12
    Online Publication Date
    2020-10-12
    Print Publication Date
    2021-02-15
    Permanent link to this record
    http://hdl.handle.net/10754/665544
    
    Metadata
    Show full item record
    Abstract
    Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With the increase of installed WWTPs worldwide, the modeling and forecast of their energy consumption have become a critical factor in WWTP design to meet environmental and economic requirements. The accurate and swift energy consumption forecasting soft-sensors are not only supportive to the daily electric and financial budgeting by WWTP practitioners on the micro-scale, but also beneficial to local municipal operation and fundamental to regional environmental impact estimation on the macro-scale. Energy consumption in WWTPs is influenced by different biological and environmental factors, making it complicated and challenging to build soft-sensors. This paper intends to provide short-term forecasting of WWTP energy consumption based on data-driven soft sensors using traditional time-series and deep learning methods. Ten data-driven soft sensors, including the ordinary least square, exponential smoothing state space, local regression, auto-regressive integrated moving average (ARIMA), structural time series model, Bayesian structural time series, non-linear auto-regressive, long short-term memory with and without updates, and gated recurrent units have been investigated and compared for WWTP energy consumption forecasting. Energy consumption time-series data from a membrane bioreactor-based WWTP in the middle east is used to evaluate the performances of the proposed soft-sensors. Results showed that ARIMA achieved slightly improved performances, among others. The employment of adaptive deep learning-based soft sensors is expected to enhance the capabilities of the deep models to quickly and accurately follow the trend of future data.
    Citation
    Harrou, F., Cheng, T., Sun, Y., Leiknes, T. O., & Ghaffour, N. (2020). A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study. IEEE Sensors Journal, 1–1. doi:10.1109/jsen.2020.3030584
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Sensors Journal
    DOI
    10.1109/JSEN.2020.3030584
    Additional Links
    https://ieeexplore.ieee.org/document/9220914/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9220914
    ae974a485f413a2113503eed53cd6c53
    10.1109/JSEN.2020.3030584
    Scopus Count
    Collections
    Articles; Biological and Environmental Science and Engineering (BESE) Division; Environmental Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC); Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.