• English
    • العربية
  • العربية 
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
    • QSpace policies
Advanced Search
Advanced Search
View Item 
  •   Qatar University QSpace
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Civil & Architectural Engineering
  • View Item
  • Qatar University QSpace
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Civil & Architectural Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm

    Thumbnail
    View/Open
    Publisher version (You have accessOpen AccessIcon)
    Publisher version (Check access options)
    Check access options
    Date
    2009-08-30
    Author
    Abu Qdais, H.
    Bani Hani, K.
    Shatnawi, N.
    Metadata
    Show full item record
    Abstract
    Artificial neural networks (ANNs) and genetic algorithms (GA) are considered among the latest tools that are used to solve complicated problems that cannot be solved by conventional solutions. The present study utilizes the ANN and GA as tools for simulating and optimizing of biogas production process from the digester of Russaifah biogas plant in Jordan. Operational data of the plant for a period of 177 days were collected and employed in the analysis. The study considered the effect of digester operational parameters, such as temperature (T), total solids (TS), total volatile solids (TVS), and pH on the biogas yield. A multi-layer ANN model with two hidden layers was trained to simulate the digester operation and to predict the methane production. The performance of the ANN model is verified and demonstrated the effectiveness of the model to predict the methane production accurately with correlation coefficient of 0.87. The developed ANN model was used with genetic algorithm to optimize the methane size. The optimal amount of methane was converged to be 77%, which is greater than the maximum value obtained from the plant records of 70.1%. The operational conditions that resulted in the optimal methane production were determined as temperature at 36 °C, TS 6.6%, TVS 52.8% and pH 6.4.
    DOI/handle
    http://dx.doi.org/10.1016/j.resconrec.2009.08.012
    http://hdl.handle.net/10576/10450
    Collections
    • Civil & Architectural Engineering [‎259 ‎ items ]

    entitlement


    QSpace is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of QSpace
      Communities & Collections Publication Date Author Title Subject Type Language
    This Collection
      Publication Date Author Title Subject Type Language

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission QSpace policies

    Help

    Item Submission Publisher policiesUser guides FAQs

    QSpace is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video