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Articles

Integrating operating conditions to develop a neural network for predicting organics removal and power density in an earthen microbial fuel cell treating leachate

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Pages 49-58 | Received 21 Jul 2022, Accepted 21 Aug 2022, Published online: 11 Sep 2022

References

  • Gurjar R, Behera M. Treatment of organic fraction of municipal solid waste in bioelectrochemical systems: a review. J. Hazardous. Toxic, Radioact. Waste. 2020;24(3):04020018.
  • Picioreanu C, Head IM, Katuri KP, et al. A computational model for biofilm-based microbial fuel cells. Water Res. 2007;41(13):2921–2940.
  • Picioreanu C, Katuri KP, Head IM, et al. Mathematical model for microbial fuel cells with anodic biofilms and anaerobic digestion. Water Sci Technol. 2008;57(7):965–971.
  • Picioreanu C, van Loosdrecht MCM, Curtis TP, et al. Model based evaluation of the effect of pH and electrode geometry on microbial fuel cell performance. Bioelectrochemistry. 2010;78(1):8–24.
  • Pinto RP, Srinivasan B, Manuel MF, et al. A two-population bio-electrochemical model of a microbial fuel cell. Bioresour Technol. 2010;101(14):5256–5265.
  • Tsompanas MA, Adamatzky A, Ieropoulos I, et al. Cellular non-linear network model of microbial fuel cell. Biosystems. 2017;156-157:53–62.
  • Tsompanas MA, Adamatzky A, Ieropoulos I, et al. Modelling microbial fuel cells using lattice boltzmann methods. IEEE/ACM Trans Comput Biol Bioinform. 2019;16(6):2035–2045.
  • Gadkari S, Gu S, Sadhukhan J. Towards automated design of bioelectrochemical systems: a comprehensive review of mathematical models. Chem. Eng. J. 2018;343:303–316.
  • Zeng Y, Choo YF, Kim BH, et al. Modelling and simulation of two-chamber microbial fuel cell. J. Power Sources. 2010;195(1):79–89.
  • Ng ANL, Kim AS. A mini-review of modeling studies on membrane bioreactor (MBR) treatment for municipal wastewaters. Desalination. 2007;212(1–3):261–281.
  • Sarmadian F, Mehrjardi RT, Sarmadian F. Modeling of some soil properties using artificial neural network and multivariate regression in gorgan province, North Iran an integrated approach of GIS and spatial data mining in big data view project sugarcane yield estimation in field scale via satellite imagery view project modeling of some soil properties using artificial neural network and multivariate regression in gorgan province. North of Iran. Glob. J. Environ. Res. 2008;2(1):30–35. https://www.researchgate.net/publication/266404114.
  • Santos FL, de Jesus VAM, Valente DSM. Modeling of soil penetration resistance using statistical analyses and artificial neural networks. Acta Sci. Agron. 2011;34(2):219–224. https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/11627.
  • Kiiza C, Pan SQ, Bockelmann-Evans B, et al. Predicting pollutant removal in constructed wetlands using artificial neural networks (ANNs). Water Sci. Eng. 2020;13(1):14–23.
  • Santoro C, Guilizzoni M, Correa Baena JP, et al. The effects of carbon electrode surface properties on bacteria attachment and start up time of microbial fuel cells. Carbon N. Y. 2014;67:128–139.
  • Li W, Cui L, Zhang Y, et al. Statistical modeling of phosphorus removal in horizontal subsurface constructed wetland. Wetlands. 2014;34(3):427–437.
  • Tardast A, Rahimnejad M, Najafpour G, et al. Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell. Fuel. 2014;117(PART A):697–703.
  • Jaeel AJ, Al-Wared AI, Ismail ZZ. Prediction of sustainable electricity generation in microbial fuel cell by neural network: Effect of anode angle with respect to flow direction. J. Electroanal. Chem. 2016;767:56–62.
  • Ismail ZZ, Al-wared AI, Jaeel AJ. Recourse recovery of bioenergy from cellulosic material in a microbial fuel cell fed with giant reed-loaded wastewater. Biofuels. 2017;10(6):737–745. Available from: 10.1080/17597269.2017.1409057.
  • Ali A, Al-Mussawy HA, Hussein MJ, et al. Experimental and theoretical study on the ability of microbial fuel cell for electricity generation. Pollution. 2018;4(2):359–368.
  • Tsompanas MA, You J, Wallis L, et al. Artificial neural network simulating microbial fuel cells with different membrane materials and electrode configurations. J. Power Sources. 2019;436:226832.
  • de Ramón-Fernández A, Salar-García MJ, Ruiz Fernández D, et al. Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells. Energy (Oxf). 2020;213:118806.
  • Li Y, Brown CW, Sun F-M, et al. Non-Invasive fermentation analysis using an artificial neural network algorithm for processing near infrared spectra. J. Near Infrared Spectrosc. 1999;7(2):101–108.
  • Feng Y, Barr W, Harper WF. Neural network processing of microbial fuel cell signals for the identification of chemicals present in water. J Environ Manage. 2013;120:84–92.
  • Sewsynker Y, G, Kana EB, Lateef A. Modelling of biohydrogen generation in microbial electrolysis cells (MECs) using a committee of artificial neural networks (ANNs). Biotechnol Biotechnol Equip. 2015;29(6):1208–1215. http://mc.manuscriptcentral.com/tbeq. 10.1080/13102818.2015.1062732.
  • Mohammed NA, Ismail ZZ. Prediction of pollutants removal from cheese industry wastewater in constructed wetland by artificial neural network. Int. J. Environ. Sci. Technol. 2021;1–16.
  • Lesnik KL, Liu H. Predicting microbial fuel cell biofilm communities and bioreactor performance using artificial neural networks. Environ Sci Technol. 2017;51(18):10881–10892.
  • Tardast A, Rahimnejad M, Najafpour G, et al. Prediction of bioelectricity production by neural network. E3 J. Biotechnol. Pharm. Res. 2012;3(3):62–68. http://www.e3journals.org/JBPR.
  • Methods S. Standard methods for examination of water and wastewater, 2005.
  • Xu SY, Lam HP, Karthikeyan OP, et al. Optimization of food waste hydrolysis in leach bed coupled with methanogenic reactor: Effect of pH and bulking agent. Bioresour Technol. 2011;102(4):3702–3708.
  • Aelterman P, Freguia S, Keller J, et al. The anode potential regulates bacterial activity in microbial fuel cells. Appl Microbiol Biotechnol. 2008;78(3):409–418.
  • Gurjar R, Shende AD, Pophali GR. Treatment of low strength wastewater using compact submerged aerobic fixed film (SAFF) reactor filled with high specific surface area synthetic media. Water Sci Technol. 2019;80(4):737–746.
  • Choudhury P, Ray RN, Bandyopadhyay TK, et al. Process engineering for stable power recovery from dairy wastewater using microbial fuel cell. Int. J. Hydrogen Energy. 2021;46(4):3171–3182.

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