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Numerical and Meta-Modeling of Nitrate Transport Reduced by Nano-Fe/Cu Particles in Packed Sand Column

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Abstract

The aim of this study is to simulate the transport of degraded nitrate by nano-Fe/Cu particles in the length of a packed sand column. A meta-modeling approach based on the adaptive neuro-fuzzy inference system (ANFIS) method and a finite volume modeling of the 1D advection–dispersion–reaction equation (ADRE) have been used for simulation of this system. A mixed order kinetic reaction model (Monod-type kinetics) has been used for sink/source term in ADRE. In the ANFIS modeling, three scenarios have been designed to determine the model’s optimum set of input–output variables. The comparison results show that in spite of capability of the ANFIS as a powerful tool for data-oriented modeling, the physical-based model for advection–dispersion–reaction phenomena is more efficient in nitrate transport simulation in the presence of nano-Fe/Cu particles.

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References

  • Anderson M.: Heat as a ground water tracer. Ground Water 43, 951–968 (2005)

    Article  Google Scholar 

  • Ataie-Ashtiani B., Hossaini S.A.: Numerical errors of explicit finite difference approximation for two-dimensional solute transport equation with linear sorption. Environ. Model. Softw. 27, 817–826 (2007)

    Google Scholar 

  • Balkhair Kh.S.: Aquifer parameters determination for large diameter wells using neural network approach. J. Hydrol. 265, 118–128 (2002)

    Article  Google Scholar 

  • Bradford S.F., Sanders B.F.: Finite-Volume models for unidirectional, nonlinear, dispersive waves. J. Waterway Port Coastal Ocean Eng. 128(4), 173–182 (2002)

    Article  Google Scholar 

  • Chang F.J., Chang Y.T.: Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Resour. 29, 1–10 (2006)

    Article  Google Scholar 

  • Chen S.H., Lin Y.H., Chang L.C., Chang F.J.: The strategy of building a flood forecast model by neuro-fuzzy network. Hydrol. Process. 20(7), 1525–1540 (2006)

    Article  Google Scholar 

  • Clauser, C. (ed): SHEMAT and PROCESSING SHEMAT—Numerical Simulation of Reactive Flow in Hot Aquifers, pp. 333. Springer, New York (2003)

    Google Scholar 

  • Dixon B.: Applicability of neuro-fuzzy technique in predicting ground-water vulnerability: A GIS-based sensitivity analysis. J. Hydrol. 309, 17–38 (2005)

    Article  Google Scholar 

  • Dou C., Woldt W., Bogardi I., Dahab M.: Numerical solute transport simulation using fuzzy sets approach. J. Contam. Hydrol. 27(1-2), 107–126 (1997)

    Article  Google Scholar 

  • Eheart, J.W., Cieniawski, S.E., Ranjithan, S.: Genetic algorithm based design of groundwater quality monitoring system. WRC research Report No. 218. Urbana: Department of Civil Engineering, The University of Illinois at Urbana-Champaign (1993)

  • Farhoudi J., Hosseini S.M., Sedghi-Asl M.: Application of neuro-fuzzy model to estimate the characteristics of local scour downstream of stilling basins. J. Hydroinform. 12(2), 201–211 (2010)

    Article  Google Scholar 

  • Fromm J.E.: A method of reducing dispersion in convective difference scheme. J. Comput. Phys. 3, 176–184 (1968)

    Article  Google Scholar 

  • Guan J., Aral M.M.: Remediation system design with multiple uncertain parameters using fuzzy sets and genetic algorithm. J. Hydrol. Eng. 10(5), 386–394 (2005)

    Article  Google Scholar 

  • Hee K.Sh., Cha D.K: Microbial reduction of nitrate in the presence of nanoscale zero-valent iron. Chemosphere 72(2), 257–262 (2008)

    Article  Google Scholar 

  • Holzbecher E.: The dynamics of subsurface water divides. Hydrol. Process. 15, 2297–2304 (2001)

    Article  Google Scholar 

  • Hosseini, S.M., Ataie-Ashtiani, B., Kholghi, M.: Nitrate reduction by nano-Fe/Cu particles in packed column. Desalination. doi:10.1016/j.desal.2011.03.051 (2011)

  • Huang Y.H., Zhang T.C.: Kinetics of nitrate reduction by iron at near neutral pH. J. Environ. Eng. 128, 604–611 (2002)

    Article  Google Scholar 

  • Huang Y.H., Zhang T.C.: Effects of low pH on nitrate reduction by iron powder. Water Res. 38, 2631–2642 (2004)

    Article  Google Scholar 

  • Huang Y.H., Zhang T.C., Shea P.J., Comfort S.D.: Effects of oxide coating and selected cations on nitrate reduction by iron metal. J. Environ. Qual. 32, 1306–1315 (2003)

    Article  Google Scholar 

  • Jang J.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybernet. 23(3), 665–685 (1993)

    Article  Google Scholar 

  • Jang J.S.R., Sun C.T.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River (1997)

    Google Scholar 

  • Jang J.S.R., Gulley N.: The Fuzzy Logic Toolbox for Use with MATLAB. The Mathworks Inc., Natick (1995)

    Google Scholar 

  • Jang J.S.R., Sun T.: Neuro-fuzzy modeling and control. Proc. IEEE 83, 378–405 (1995)

    Article  Google Scholar 

  • Kadu B.S., Sathe Y.D., Ingle A.B., Chikate R.C., Patil K.R., Rode C.V.: Efficiency and recycling capability of montmorillonite supported Fe/Ni bimetallic nanocomposite towards hexavalent chromium remediation. Appl. Catal. B 104, 407–414 (2011)

    Article  Google Scholar 

  • Kanel S.R., Nepal D., Manning B., Choi H.: Transport of surfacemodified iron nanoparticle in porous media and application to arsenic(III) remediation. J. Nanopart. Res. 9, 725–735 (2007)

    Article  Google Scholar 

  • Kazemi S.M., Hosseini S.M.: Comparison of spatial interpolation methods for estimating heavy metals in sediments of Caspian Sea. Expert Syst. Appl. 38, 1632–1649 (2011)

    Article  Google Scholar 

  • Kholghi M., Hosseini S.M.: Comparison of groundwater level estimation using neuro-fuzzy and ordinary kriging. Environ. Model Assess. 14, 729–737 (2009)

    Article  Google Scholar 

  • Kleijnen J.P.C., Sargent R.G.: A methodology for fitting and validating metamodels in simulation. Eur. J. Oper. Res. 120, 14–29 (2000)

    Article  Google Scholar 

  • Lee E.S.: Neuro-fuzzy estimation in spatial statistics. J. Math. Anal. Appl. 249, 221–231 (2000). doi:10.1006/jmaa.2000.6938.13

    Article  Google Scholar 

  • Liu J.J., Soni B.K.: 2D groundwater contaminant transport modeling by using the finite volume method on an unstructured grid system. Appl. Math. Comput. 89(1–3), 199–211 (1998)

    Article  Google Scholar 

  • Luchetta A., Manetti S.: A real time hydrological forecasting system using a fuzzy clusterin1g approach. Comput. Geosci. 29(9), 1111–1117 (2003)

    Article  Google Scholar 

  • Mukopadhyay A.: Spatial estimation of tansmissivity using artificial neural network. Ground Water 37(3), 458–464 (1999)

    Article  Google Scholar 

  • Nagpal, V., Bokare, A.D., Chikate, R.C., Rode, C.V., Paknikar, K.M.: Reply to comment on “Reductive dechronation of gamma-hexachlorocyclohexane using Fe/Pd bimetallic nano particles”, by C. Noubactep. J. Hazard. Mater. doi:10.106/j.jhazmat.2011.04.015 (2011)

  • Namin M., Lin B., Falconer R.A.: Modelling estuarine and coastal flows using an unstructured triangular finite volume algorithm. Adv. Water Resour. 27(12), 1179–1197 (2004)

    Article  Google Scholar 

  • Nayak P.C., Sudheer K.P., Rangan D.M., Ramasastri K.S.: A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol. 291, 52–66 (2004)

    Article  Google Scholar 

  • Putti M., Yeh W.G., Mulder W.A.: A triangular finite volume approach with high-resolution up-wind terms for the solution of groundwater transport equations. Water Resour. Res. 26(12), 2865–2880 (1990)

    Google Scholar 

  • Ruangchainikom C., Liao C.H., Anotai J., Lee M.T.: Characteristics of nitrate reduction by zero-valent iron powder in the recirculated and CO2-bubbled system. Water Res. 40, 195–204 (2006)

    Article  Google Scholar 

  • Saleh N., Sirk K., Liu Y., Phenrat T., Dufour B., Matyjaszeski K., Tilton R.D., Lowry G.V.: Surface modifications enhance nanoiron transport and NAPL targeting in saturated porous media. Environ. Eng. Sci. 24, 45–57 (2007)

    Article  Google Scholar 

  • Shigidi A., Garcia L.A.: Parameter Estimation in Groundwater Hydrology Using Artificial Neural Networks. J. Comput. Civil Eng. 17(4), 281–289 (2003)

    Article  Google Scholar 

  • Singh S.K.: Estimating dispersion coefficient and porosity from soil column tests. J. Environ. Eng. 128(11), 1095–1099 (2002)

    Article  Google Scholar 

  • Stanbro W.D.: Modeling the interaction of peroxynitrite in lowdensity lipoprotein particles. J. Theor. Biol. 205, 465–471 (2000)

    Article  Google Scholar 

  • Stenemo F., Lindahl A.M.L., Gärdenäs A., Jarvis N.: Meta-modeling of the pesticide fate model MACRO for groundwater exposure assessments using artificial neural networks. J. Contam. Hydrol. 93, 270–283 (2007)

    Article  Google Scholar 

  • Sudheer Ch., Mathur Sh.: Modeling uncertainty analysis in flow and solute transport model using adaptive neuro fuzzy inference system and particle swarm optimization. KSCE J. Civil Eng. 14(6), 941–951 (2010)

    Article  Google Scholar 

  • Takagi T., Sugeno M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybernet. 15(1), 116–132 (1985)

    Google Scholar 

  • Tosco T., Sethi R.: Transport of non-Newtonian suspensions of highly concentrated micro- and nanoscale iron particles in porous media: a modeling approach. Environ. Sci. Technol. 44(23), 9062–9068 (2010)

    Article  Google Scholar 

  • Tratnyek P.G., Scherer M.M., Johnson T.J., Matheson L.J.: Permeable reactive barriers of iron and other zero-valent metals. In: Tarr, M.A. (ed) Chemical Degradation Methods for Wastes and Pollutants: Environmental and Industrial Applications, pp. 371–421. Marcel Dekker, New York (2003)

    Google Scholar 

  • Tufenkji N., Elimelech M.: Correlation equation for predicting single-collector efficiency in physicochemical filtration in saturated porous media. Environ. Sci. Technol. 38, 529–536 (2004)

    Article  Google Scholar 

  • Tutmez B., Hatipoglu Z., Kaymak U.: Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Comput. Geosci. 32, 421–433 (2006)

    Article  Google Scholar 

  • Vosoughifar H.R., Shamsai A., Ebadi T.: Discretization of flow contain nitrate in porous media by finite volume technique. Int. J. Environ. Sci. Technol. 1(4), 317–323 (2005)

    Google Scholar 

  • Wang W., Zhang J., Li T., Zhang H., Gao S.: Preparation of spherical iron nanoclusters in ethanol–water solution for nitrate removal. Chemosphere 65(8), 1396–1404 (2006)

    Article  Google Scholar 

  • Xu Y., Zhang W.X.: Subcolloidal Fe/Ag particles for reductive de halogenation of chlorinated benzenes. Ind. Eng. Chem. Res. 39, 2238–2244 (2000)

    Article  Google Scholar 

  • Yan, Sh.: Optimizing groundwater remediation design using dynamic meta-models and genetic algorithms, Thesis dissertation, University of Illinois at Urbana-Champaign, p. 186 (2006)

  • Zhang W.X.: Nanoscale iron particles for environmental remediation: An overview. J. Nanoparticle Res. 5, 323 (2003)

    Article  Google Scholar 

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Correspondence to Seiyed Mossa Hosseini.

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Hosseini, S.M., Kholghi, M. & Vagharfard, H. Numerical and Meta-Modeling of Nitrate Transport Reduced by Nano-Fe/Cu Particles in Packed Sand Column. Transp Porous Med 94, 149–174 (2012). https://doi.org/10.1007/s11242-012-9994-z

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