• Login
    View Item 
    •   DSpace Home
    • 2-DERGİLER
    • 03) Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
    • Cilt 12, Sayı 4 (2023)
    • View Item
    •   DSpace Home
    • 2-DERGİLER
    • 03) Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
    • Cilt 12, Sayı 4 (2023)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Theoretical Models Constructed by Artificial Intelligence Algorithms for Enhanced Lipid Production: Decision Support Tools

    Thumbnail
    View/Open
    Tam Metin/Full Text (1.553Mb)
    Date
    2023
    Author
    ONAY, Aytun
    Metadata
    Show full item record
    Abstract
    Theoretical models that predict the lipid content of microalgae are an important tool for increasing lipid productivity. In this study, response surface methodology (RSM), RSM combined with artificial neural network (ANN), and RSM combined with ensemble learning algorithms (ELA) for regression were used to calculate the maximum lipid percentage (%) from Chlorella minutissima (C. minutissima). We defined one set of rules to achieve the highest lipid content and used trees.RandomTree (tRT) to simulate the process parameters under various conditions. Among the various models, results showed the optimum values of the root mean squared error (0.2156), mean absolute error (0.1167), and correlation coefficient (0.9961) in the tRT model. RSM combined with tRT estimated that the lipid percentage was 30.3% in wastewater (< 35%), lysozyme (≥ 3.5 U/mL), and chitinase (< 15 U/mL) concentrations, achieving the best model based on experimental data. The optimal values of wastewater concentration, chitinase, and lysozyme were 20% (v/v), 5 U/mL, and 10 U/mL, respectively. Also, the if-then rules obtained from tRT were also used to test the process parameters. The tRT model served as a powerful tool to obtain maximum lipid content. The final rankings of the performance of various algorithms were determined. Furthermore, the models developed can be used by the fuel industry to achieve cost-effective, large-scale production of lipid content and biodiesel
    URI
    http://dspace.beu.edu.tr:8080/xmlui/handle/123456789/14865
    Collections
    • Cilt 12, Sayı 4 (2023) [32]





    Creative Commons License
    DSpace@BEU by Bitlis Eren University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     




    | Yönerge | Rehber | İletişim |

    sherpa/romeo

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV