Abstract
Complexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated. Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent and type of counter ion) was established. Two approaches of preparation of input data and two different artificial neural network architectures, general regression neural network and back-propagation neural network have been used for modeling of experimental data. The extrapolation ability of selected architectures, i.e., the prediction of rejection coefficient with inputs beyond the calibration range of original model, was also determined. The predictions were successful, and after evaluation of performances, the models that were developed gave relatively good results of mean absolute percentage error from 4 to 14% and R-squared from 0.717 to 0.852 for general regression neural network and from 0.897 to 0.955 for back-propagation neural network.
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Abbreviations
- P :
-
Operating pressure
- pH:
-
pH value
- C HM :
-
Concentration of heavy metal
- C CA :
-
Concentration of complexing agent
- R :
-
Rejection coefficient
- C p :
-
Concentration of heavy metal ion in permeate
- C f :
-
Concentration of heavy metal in feed solution
- n tr :
-
Number of training cases
- n wb :
-
Number of weights and biases
- N h :
-
Number of hidden neurons
- D j :
-
Euclidian distance
- W ij :
-
Weights
- f(D j):
-
Exponential function
- S 1 :
-
Summation unit for calculating sums of weighted outputs
- S 2 :
-
Summation unit for calculating sums of unweighted outputs
- ISF:
-
Individual smoothing factor
- RCA:
-
Relative contribution factor
- MAPE:
-
Mean absolute percentage error
- RMSE:
-
Root mean-squared error
- PBIAS:
-
Percent bias PBIAS
- O i :
-
Observed value
- P i :
-
Predicted value
- 1st IEHM :
-
First ionization energy
- r cov,HM :
-
Covalent diameter
- M HM :
-
Molar mass of used heavy metal solution
- M CA :
-
Molar mass of used complexing agent
- CpHM :
-
Concentration of heavy metal ion in permeate
References
Al-Zoubi H, Ibrahim KA, Abu-Sbeih KA (2015) Removal of heavy metals from wastewater by economical polymeric collectors using dissolved air flotation process. J Water Process Eng 8:19–27. doi:10.1016/j.jwpe.2015.08.002
Antanasijevic D, Pocajt V, Peric-Grujic A, Ristic M (2014) Modelling of dissolved oxygen in the danube river using artificial neural networks and Monte carlo simulation uncertainty analysis. J Hydrol 519:1895–1907. doi:10.1016/j.jhydrol.2014.10.009
Aydiner C, Demir I, Yildiz E (2005) Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal. J Membr Sci 248:53–62. doi:10.1016/j.memsci.2004.07.036
Bagheri M, Mirbagheri SA, Ehteshami M, Bagheri Z (2015) Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks. Process Saf Environ Prot 93:111–123. doi:10.1016/j.psep.2014.04.006
Cabral M, Toure A, Garçon G et al (2015) Effects of environmental cadmium and lead exposure on adults neighboring a discharge: evidences of adverse health effects. Environ Pollut 206:247–255. doi:10.1016/j.envpol.2015.06.032
Camarillo R, Llanos J, García-Fernández L et al (2010) Treatment of copper (II)-loaded aqueous nitrate solutions by polymer enhanced ultrafiltration and electrodeposition. Sep Purif Technol 70:320–328. doi:10.1016/j.seppur.2009.10.014
Chang YF, Wen JF, Cai JF et al (2012) An investigation and pathological analysis of two fatal cases of cadmium poisoning. Forensic Sci Int 220:e5–e8. doi:10.1016/j.forsciint.2012.01.032
Choi YJ, Oh H, Lee S et al (2012) Investigation of the filtration characteristics of pilot-scale hollow fiber submerged MF system using cake formation model and artificial neural networks model. Desalination 297:20–29. doi:10.1016/j.desal.2012.04.013
Ennigrou DJ, Gzara L, Ben Romdhane MR, Dhahbi M (2009) Retention of cadmium ions from aqueous solutions by poly(ammonium acrylate) enhanced ultrafiltration. Chem Eng J 155:138–143. doi:10.1016/j.cej.2009.07.028
Graillot A, Cojocariu C, Bouyer D et al (2015) Thermosensitive polymer enhanced filtration (TEF) process: an innovative process for heavy metals removal and recovery from industrial wastewaters. Sep Purif Technol 141:17–24. doi:10.1016/j.seppur.2014.11.023
Heddam S (2014) Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA. Environ Technol 35:1650–1657. doi:10.1080/09593330.2013.878396
Hosny WM, Basta AH, El-Saied H (1997) Metal chelates with some cellulose derivatives: V. synthesis and characterization of some iron(III) complexes with cellulose ethers. Polymer Int 42:157–162. doi:10.1002/(SICI)1097-0126(199702)42:2<157:AID-PI632>3.0.CO;2-7
Huang Y, Wu D, Wang X et al (2016) Removal of heavy metals from water using polyvinylamine by polymer-enhanced ultrafiltration and flocculation. Sep Purif Technol 158:124–136. doi:10.1016/j.seppur.2015.12.008
Johnson SR, Jurs PC (1999) Prediction of the clearing temperatures of a series of liquid crystals from molecular structure. Chem Mater 11:1007–1023. doi:10.1021/cm980674x
Kalogirou SA (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Progress Energy Combust Sci 29:515–566. doi:10.1016/S0360-1285(03)00058-3
Liu Q-F, Kim S-H, Lee S (2009) Prediction of microfiltration membrane fouling using artificial neural network models. Sep Purif Technol 70:96–102. doi:10.1016/j.seppur.2009.08.017
Madaeni SS, Hasankiadeh NT, Kurdian AR, Rahimpour A (2010) Modeling and optimization of membrane fabrication using artificial neural network and genetic algorithm. Sep Purif Technol 76:33–43. doi:10.1016/j.seppur.2010.09.017
Mähler J, Persson I (2012) A study of the hydration of the alkali metal ions in aqueous solution. Inorg Chem 51:425–438. doi:10.1021/ic2018693
Melesse AM, Ahmad S, McClain ME et al (2011) Suspended sediment load prediction of river systems: an artificial neural network approach. Agric Water Manag 98:855–866. doi:10.1016/j.agwat.2010.12.012
Mirzaei M, Behzadi M, Abadi NM, Beizaei A (2011) Simultaneous separation/preconcentration of ultra trace heavy metals in industrial wastewaters by dispersive liquid–liquid microextraction based on solidification of floating organic drop prior to determination by graphite furnace atomic absorption spectrometry. J Hazard Mater 186:1739–1743. doi:10.1016/j.jhazmat.2010.12.080
Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900
Nandi BK, Moparthi A, Uppaluri R, Purkait MK (2010) Treatment of oily wastewater using low cost ceramic membrane: comparative assessment of pore blocking and artificial neural network models. Chem Eng Res Design 88:881–892. doi:10.1016/j.cherd.2009.12.005
Pi JK, Yang HC, Wan LS et al (2016) Polypropylene microfiltration membranes modified with TiO2 nanoparticles for surface wettability and antifouling property. J Membr Sci 500:8–15. doi:10.1016/j.memsci.2015.11.014
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning represetations by back-propagating errors. Nature 323:533–536. doi:10.1038/323533a0
Shao J, Qin S, Davidson J et al (2013) Recovery of nickel from aqueous solutions by complexation-ultrafiltration process with sodium polyacrylate and polyethylenimine. J Hazard Mater 244–245:472–477. doi:10.1016/j.jhazmat.2012.10.070
Sing KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality—a case study. Ecol Model 220:888–895. doi:10.1016/j.ecolmodel.2009.01.004
Specht DF (1991) The general regression neural network. IEEE Trans Neural Netw 2:568–576. doi:10.1109/72.97934
Thwin MMT, Quah T-S (2005) Application of neural networks for software quality prediction using object-oriented metrics. J Syst Softw 76:147–156. doi:10.1016/j.jss.2004.05.001
Trivunac K, Sekulic Z, Stevanovic S (2012) Zinc removal from wastewater by a complexation-microfiltration process. J Serb Chem Soc 77:1661–1670
Wang Y, Zheng T, Zhao Y et al (2013) Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China. Environ Sci Pollut Res 20:8909–8923. doi:10.1007/s11356-013-1874-8
Woznicki SA, Nejadhashemi AP, Abouali M et al (2016) Ecohydrological modeling for large-scale environmental impact assessment. Sci Total Environ 543:274–286. doi:10.1016/j.scitotenv.2015.11.044
Xi X, Cui Y, Wang Z et al (2011) Study of dead-end microfiltration features in sequencing batch reactor (SBR) by optimized neural networks. Desalination 272:27–35. doi:10.1016/j.desal.2010.12.049
Zeng G, Liu Y, Tang L et al (2015) Enhancement of Cd(II) adsorption by polyacrylic acid modified magnetic mesoporous carbon. Chem Eng J 259:153–160. doi:10.1016/j.cej.2014.07.115
Acknowledgement
The authors acknowledge financial support from the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. OI172007.
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Sekulić, Z., Antanasijević, D., Stevanović, S. et al. Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process. Int. J. Environ. Sci. Technol. 14, 1383–1396 (2017). https://doi.org/10.1007/s13762-017-1248-8
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DOI: https://doi.org/10.1007/s13762-017-1248-8