IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v134y2017icp136-156.html
   My bibliography  Save this article

Design of explicit models for estimating efficiency characteristics of microbial fuel cells

Author

Listed:
  • Garg, A.
  • Lam, Jasmine Siu Lee

Abstract

Recent years have seen the use of microbial fuel cells for the generation of electricity from wastewater and renewable biomass. The efficiency characteristics (power density and voltage output) of fuel cells depend highly on their operating conditions such as current density, chemical oxygen demand concentration and anolyte concentration. Computational intelligence methods based on genetic programming and multi-adaptive regression splines are proposed in design of explicit models for estimating efficiency characteristics of microfluidic microbial fuel cells based on the operating conditions. Performance of the models evaluated against the actual data reveals that the models formulated from genetic programming outperform the multi-adaptive regression splines models. The robustness in the best models is validated by performing simulation of the models over 8000 runs based on the normal distribution of the operating conditions. 2-D and 3-D surface analysis conducted on the models reveals that the power density of the fuel cell increases with an increase in values of chemical oxygen demand concentration and current density till a certain value and then decreases. The voltage output decreases with an increase in values of current density while increases with an increase in values of chemical oxygen demand concentration to a certain limit.

Suggested Citation

  • Garg, A. & Lam, Jasmine Siu Lee, 2017. "Design of explicit models for estimating efficiency characteristics of microbial fuel cells," Energy, Elsevier, vol. 134(C), pages 136-156.
    • Handle: RePEc:eee:energy:v:134:y:2017:i:c:p:136-156
    DOI: 10.1016/j.energy.2017.05.180
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217308770
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.05.180?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    2. Shamshirband, Shahaboddin & Petković, Dalibor & Amini, Amineh & Anuar, Nor Badrul & Nikolić, Vlastimir & Ćojbašić, Žarko & Mat Kiah, Miss Laiha & Gani, Abdullah, 2014. "Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission," Energy, Elsevier, vol. 67(C), pages 623-630.
    3. Singh, Satyender & Dhiman, Prashant, 2014. "Thermal and thermohydraulic performance evaluation of a novel type double pass packed bed solar air heater under external recycle using an analytical and RSM (response surface methodology) combined ap," Energy, Elsevier, vol. 72(C), pages 344-359.
    4. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    5. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    6. Oliveira, V.B. & Simões, M. & Melo, L.F. & Pinto, A.M.F.R., 2013. "A 1D mathematical model for a microbial fuel cell," Energy, Elsevier, vol. 61(C), pages 463-471.
    7. Wang, Ji-min & Lan, Shen & Li, Wen-ke, 2014. "Numerical simulation and process optimization of an aluminum holding furnace based on response surface methodology and uniform design," Energy, Elsevier, vol. 72(C), pages 521-535.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mojeed Opeyemi Oyedeji & Abdullah Alharbi & Mujahed Aldhaifallah & Hegazy Rezk, 2023. "Optimal Data-Driven Modelling of a Microbial Fuel Cell," Energies, MDPI, vol. 16(12), pages 1-21, June.
    2. Chen, Bor-Yann & Liao, Jia-Hui & Hsueh, Chung-Chuan & Qu, Ziwei & Hsu, An-Wei & Chang, Chang-Tang & Zhang, Shuping, 2018. "Deciphering biostimulation strategy of using medicinal herbs and tea extracts for bioelectricity generation in microbial fuel cells," Energy, Elsevier, vol. 161(C), pages 1042-1054.
    3. Zinadini, S. & Zinatizadeh, A.A. & Rahimi, M. & Vatanpour, V. & Bahrami, K., 2017. "Energy recovery and hygienic water production from wastewater using an innovative integrated microbial fuel cell–membrane separation process," Energy, Elsevier, vol. 141(C), pages 1350-1362.

    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. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
    2. Rashidi, S. & Bovand, M. & Abolfazli Esfahani, J., 2015. "Structural optimization of nanofluid flow around an equilateral triangular obstacle," Energy, Elsevier, vol. 88(C), pages 385-398.
    3. Shiravi, Amir Hossein & Firoozzadeh, Mohammad & Lotfi, Marzieh, 2022. "Experimental study on the effects of air blowing and irradiance intensity on the performance of photovoltaic modules, using Central Composite Design," Energy, Elsevier, vol. 238(PA).
    4. Krishnamoorthi, M. & Malayalamurthi, R., 2018. "Engine characteristics analysis of chaulmoogra oil blends and corrosion analysis of injector nozzle using scanning electron microscopy/energy dispersive spectroscopy," Energy, Elsevier, vol. 165(PB), pages 1292-1319.
    5. Shen-Tsu Wang, 2016. "Integrating grey sequencing with the genetic algorithm--immune algorithm to optimise touch panel cover glass polishing process parameter design," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4882-4893, August.
    6. Yek, Peter Nai Yuh & Cheng, Yoke Wang & Liew, Rock Keey & Wan Mahari, Wan Adibah & Ong, Hwai Chyuan & Chen, Wei-Hsin & Peng, Wanxi & Park, Young-Kwon & Sonne, Christian & Kong, Sieng Huat & Tabatabaei, 2021. "Progress in the torrefaction technology for upgrading oil palm wastes to energy-dense biochar: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    7. Qin, Caiyan & Kim, Joong Bae & Lee, Bong Jae, 2019. "Performance analysis of a direct-absorption parabolic-trough solar collector using plasmonic nanofluids," Renewable Energy, Elsevier, vol. 143(C), pages 24-33.
    8. Ramos, Ana & Monteiro, Eliseu & Rouboa, Abel, 2019. "Numerical approaches and comprehensive models for gasification process: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 188-206.
    9. M'Arimi, M.M. & Mecha, C.A. & Kiprop, A.K. & Ramkat, R., 2020. "Recent trends in applications of advanced oxidation processes (AOPs) in bioenergy production: Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    10. Renzi, Massimiliano & Bietresato, Marco & Mazzetto, Fabrizio, 2016. "An experimental evaluation of the performance of a SI internal combustion engine for agricultural purposes fuelled with different bioethanol blends," Energy, Elsevier, vol. 115(P1), pages 1069-1080.
    11. Chamberlin Stéphane Azebaze Mboving & Zbigniew Hanzelka & Andrzej Firlit, 2022. "Analysis of the Factors Having an Influence on the LC Passive Harmonic Filter Work Efficiency," Energies, MDPI, vol. 15(5), pages 1-51, March.
    12. Lu Chen & Qincheng Chen & Pinhua Rao & Lili Yan & Alghashm Shakib & Guoqing Shen, 2018. "Formulating and Optimizing a Novel Biochar-Based Fertilizer for Simultaneous Slow-Release of Nitrogen and Immobilization of Cadmium," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    13. Biranchi Panda & K. Shankhwar & Akhil Garg & M. M. Savalani, 2019. "Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 809-820, February.
    14. Zahedi, Ali Reza & Mirnezami, Seyed Abolfazl, 2020. "Experimental analysis of biomass to biodiesel conversion using a novel renewable combined cycle system," Renewable Energy, Elsevier, vol. 162(C), pages 1177-1194.
    15. Ahmad Abbaszadeh-Mayvan & Barat Ghobadian & Gholamhassan Najafi & Talal Yusaf, 2018. "Intensification of Continuous Biodiesel Production from Waste Cooking Oils Using Shockwave Power Reactor: Process Evaluation and Optimization through Response Surface Methodology (RSM)," Energies, MDPI, vol. 11(10), pages 1-13, October.
    16. de Oliveira, Lucas Guedes & Aquila, Giancarlo & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & de Queiroz, Anderson Rodrigo & de Oliveira Pamplona, Edson & Camatta, Ulisses Pessin, 2020. "Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    17. Theofilos Kamperidis & Asimina Tremouli & Antonis Peppas & Gerasimos Lyberatos, 2022. "A 2D Modelling Approach for Predicting the Response of a Two-Chamber Microbial Fuel Cell to Substrate Concentration and Electrolyte Conductivity Changes," Energies, MDPI, vol. 15(4), pages 1-15, February.
    18. Gorji, Tahereh B. & Ranjbar, A.A., 2017. "Thermal and exergy optimization of a nanofluid-based direct absorption solar collector," Renewable Energy, Elsevier, vol. 106(C), pages 274-287.
    19. Trung-Thanh Nguyen & Mozammel Mia, 2020. "Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties," Energies, MDPI, vol. 13(18), pages 1-20, September.
    20. Masooma Munir & Muhammad Nadeem & Barkat Ali & Muhammad Sultan & Rabia Kanwal & Huda Abdalrahman Al-Jumayi & Eman Hassan Ahmed Algarni & Maged B. Alnofeai & Samy F. Mahmoud, 2022. "Investigating the Impact of Ultrasound, Microwave, and High-Pressure Processing of Milk on the Volatile Compounds and Sensory Properties of Cheddar Cheese," Agriculture, MDPI, vol. 12(5), pages 1-17, April.

    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:eee:energy:v:134:y:2017:i:c:p:136-156. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.