IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1312-d1031077.html
   My bibliography  Save this article

A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence

Author

Listed:
  • Yaser Abdollahfard

    (Petroleum Engineering Department, Amirkabir University of Technology, Tehran 158754413, Iran)

  • Mehdi Sedighi

    (Department of Chemical Engineering, University of Qom, Qom 3716146611, Iran)

  • Mostafa Ghasemi

    (Chemical Engineering Section, Faculty of Engineering, Sohar University, Sohar 311, Oman)

Abstract

Microbial fuel cells have recently received considerable attention as a potential source of renewable energy. Due to its complex and hybrid nature, it has significant nonlinear features and substantial hysteresis behavior, making it hard to optimize and control its power generation directly. This study modeled power density and COD removal using random forest regression and gradient boost regression trees. System inputs are three key parameters that affect performance and commercialization. There is a range of 0.1–0.5 mg/cm 2 of Pt, a degree of sulfonation of sulfonated polyether-etherketone varying from 20% to 80%, and a cathode aeration rate of 10–150 mL/min. Based on the model’s accuracies, gradient boost regression was selected for power density prediction and random forest for COD removal prediction. Particle swarm optimization was used as the optimization algorithm after selecting the best models to maximize COD removal and power density. It was found that DS was the most critical parameter for COD removal, and Pt was the most critical parameter for power density. There is a different optimal input value for each model. In order to maximize power density, DS (%) must be 67.7087, Pt (mg/cm 2 ) must be 0.3943, and Aeration (mL/min) must be 117.7192. To maximize COD removal, the DS (%) must be 75.8816, the Pt (mg/cm 2 ) must be 0.3322, and the Aeration (mL/min) must be 75.1933.

Suggested Citation

  • Yaser Abdollahfard & Mehdi Sedighi & Mostafa Ghasemi, 2023. "A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1312-:d:1031077
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1312/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1312/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abed Alaswad & Abdelnasir Omran & Jose Ricardo Sodre & Tabbi Wilberforce & Gianmichelle Pignatelli & Michele Dassisti & Ahmad Baroutaji & Abdul Ghani Olabi, 2020. "Technical and Commercial Challenges of Proton-Exchange Membrane (PEM) Fuel Cells," Energies, MDPI, vol. 14(1), pages 1-21, December.
    2. Mostafa Ghasemi & Mehdi Sedighi & Yie Hua Tan, 2021. "Carbon Nanotube/Pt Cathode Nanocomposite Electrode in Microbial Fuel Cells for Wastewater Treatment and Bioenergy Production," Sustainability, MDPI, vol. 13(14), pages 1-13, July.
    3. Janitza, Silke & Tutz, Gerhard & Boulesteix, Anne-Laure, 2016. "Random forest for ordinal responses: Prediction and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 57-73.
    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. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 885-915, December.
    2. Buczak, Philip & Horn, Daniel & Pauly, Markus, 2024. "Old but Gold or New and Shiny? Comparing Tree Ensembles for Ordinal Prediction with a Classic Parametric Approach," OSF Preprints v7bcf, Center for Open Science.
    3. Roman Hornung, 2020. "Ordinal Forests," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 4-17, April.
    4. Marcella Corduas & Alfonso Piscitelli, 2017. "Modeling university student satisfaction: the case of the humanities and social studies degree programs," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 617-628, March.
    5. Segundo Rojas-Flores & Magaly De La Cruz-Noriega & Luis Cabanillas-Chirinos & Renny Nazario-Naveda & Moisés Gallozzo-Cardenas & Félix Diaz & Emzon Murga-Torres, 2023. "Potential Use of Coriander Waste as Fuel for the Generation of Electric Power," Sustainability, MDPI, vol. 15(2), pages 1-10, January.
    6. Gairaa, Kacem & Voyant, Cyril & Notton, Gilles & Benkaciali, Saïd & Guermoui, Mawloud, 2022. "Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities," Renewable Energy, Elsevier, vol. 183(C), pages 890-902.
    7. Abdul Ghani Olabi & Enas Taha Sayed, 2023. "Developments in Hydrogen Fuel Cells," Energies, MDPI, vol. 16(5), pages 1-5, March.
    8. Wang, Yong & Ma, Yinjie & Xie, Deyi & Yu, Zhenhuan & E, Jiaqiang, 2021. "Numerical study on the influence of gasoline properties and thermodynamic conditions on premixed laminar flame velocity at multiple conditions," Energy, Elsevier, vol. 233(C).
    9. Odey Alshboul & Ali Shehadeh & Ghassan Almasabha & Ali Saeed Almuflih, 2022. "Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, May.
    10. Aleix Alcacer & Irene Epifanio & Jorge Valero & Alfredo Ballester, 2021. "Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size," Mathematics, MDPI, vol. 9(7), pages 1-15, April.
    11. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
    12. García-Salaberri, Pablo A. & Sánchez-Ramos, Arturo, 2024. "Modeling of a polymer electrolyte membrane fuel cell with a hybrid continuum/discrete formulation at the rib/channel scale: Effect of relative humidity and temperature on performance and two-phase tra," Applied Energy, Elsevier, vol. 367(C).
    13. Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
    14. Maljkovic, Danica & Basic, Bojana Dalbelo, 2020. "Determination of influential parameters for heat consumption in district heating systems using machine learning," Energy, Elsevier, vol. 201(C).
    15. Apostolos G. Katsafados & Dimitris Anastasiou, 2024. "Short-term prediction of bank deposit flows: do textual features matter?," Annals of Operations Research, Springer, vol. 338(2), pages 947-972, July.
    16. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.
    17. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
    18. Mingzhang Pan & Chengjie Pan & Jinyang Liao & Chao Li & Rong Huang & Qiwei Wang, 2021. "Assessment of Sensitivity to Evaluate the Impact of Operating Parameters on Stability and Performance in Proton Exchange Membrane Fuel Cells," Energies, MDPI, vol. 14(14), pages 1-23, July.
    19. Yifei Jiang & Honglei Zhang & Xianting Cao & Ge Wei & Yang Yang, 2023. "How to better incorporate geographic variation in Airbnb price modeling?," Tourism Economics, , vol. 29(5), pages 1181-1203, August.
    20. Ghasemi, Mostafa & Rezk, Hegazy, 2024. "Performance improvement of microbial fuel cell using experimental investigation and fuzzy modelling," Energy, Elsevier, vol. 286(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:15:y:2023:i:2:p:1312-:d:1031077. 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.