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August  2019, 2(3): 203-213. doi: 10.3934/mfc.2019014

Data modeling analysis on removal efficiency of hexavalent chromium

1. 

School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China

2. 

Key Laboratory of Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing 100124, China

3. 

State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China

4. 

Key Laboratory of Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Environmental Protection Research Institute of Light Industry, Beijing 100124, China

* Corresponding author: Dong Li

Received  April 2019 Revised  June 2019 Published  September 2019

Chromium and its compounds are widely used in many industries in China and play a very important role in the national economy. At the same time, heavy metal chromium pollution poses a great threat to the ecological environment and human health. Therefore, it's necessary to safely and effectively remove the chromium from pollutants. In practice, there are many factors which influence the removal efficiency of the chromium. However, few studies have investigated the relationship between multiple factors and the removal efficiency of the chromium till now. To this end, this paper uses the green synthetic iron nanoparticles to remove the chromium and investigates the impacts of multiple factors on the removal efficiency of the chromium. A novel model that maps multiple given factors to the removal efficiency of the chromium is proposed through the advanced machine learning methods, i.e., XGBoost and random forest (RF). Experiments demonstrate that the proposed method can predict the removal efficiency of the chromium precisely with given influencing factors, which is very helpful for finding the optimal conditions for removing the chromium from pollutants.

Citation: Runqin Hao, Guanwen Zhang, Dong Li, Jie Zhang. Data modeling analysis on removal efficiency of hexavalent chromium. Mathematical Foundations of Computing, 2019, 2 (3) : 203-213. doi: 10.3934/mfc.2019014
References:
[1]

R. Benniceli, Z. Stpniewska, A. Banach, et al., The Ability of Azolla Caroliniana to Remove Heavy Metals (Hg(Ⅱ), Cr(Ⅲ), Cr(Ⅵ)) from Municipal Waste Water, Chemosphere, 55 (2004), 141–146.Google Scholar

[2]

S. Bosinco, J. Roussy, E. Guibal, et al., Interaction Mechanisms between Hexavalent Chromium and Corncob, Environmental Technology, 17 (1996), 55–62.Google Scholar

[3]

L. Breiman, Random forest, Machine Learning, 45 (2001), 5-32. Google Scholar

[4]

T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system[C]. Acm sigkdd international conference on knowledge discovery and data mining, Water Research, (2016), 785–794.Google Scholar

[5]

M. ChenQ. LiuS. ChenY. LiuC. Zhang and R. Liu, XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system, IEEE Access, 7 (2019), 13149-13158. doi: 10.1109/ACCESS.2019.2893448. Google Scholar

[6]

J. E. Cruver, Reverse Osmosis-Where it Stands Today, Water Sewage Works, 1973.Google Scholar

[7]

V. Dushenkov, P. B. A. N. Kumar, H. Motto, et al., Rhizofiltration: The use of plants to remove heavy metals from aqueous streams, Environmental Science & Technology, 29 (1995), 1239–1245.Google Scholar

[8]

I. C. Eromosele and S. S. Bayero, Adsorption of chromium and zinc ions from aqueous solutions by cellulosic graft copolymers, Bioresource Technology, 71 (2000), 279-281. Google Scholar

[9]

A. M. Farag, T. May, G. D. Marty, et al., The effect of chronic chromium exposure on the health of chinook salmon (Oncorhynchus Tshawytscha), Aquat Toxicol, 76 (2006), 246–257.Google Scholar

[10]

J. Farrell, J. P. Wang, P. O'Day, et al., Electrochemical and Spectroscopic Study of Arsenate Removal from Water Using Zero-Valent Iron Media, Environmental Science & Technology, 35 (2001), 2026–2032.Google Scholar

[11]

S. Gandhi, B. T. Oh, J. L. Schnoor, et al., Degradation of TCE, Cr(Ⅵ), Sulfate, and nitrate mixtures by granular iron in flow-through columns under different microbial conditions, Water Research, 36 (2002), 1973–1982.Google Scholar

[12]

C. R. gibbs, Characterization and application of ferrozine iron reagent as a ferrous iron indicator, Anal.Chem., 48 (1976), 1197-1200. Google Scholar

[13]

R. W. Gillham And S. F. O'Hannesin, Enhanced degradation of halogenated aliphatics by zero-valent iron, Ground Water, 32 (1994), 958.Google Scholar

[14]

T. KarthikeyanS. Rajgopal and L. R. Miranda, Chromium(Ⅵ) adsorption from aqueous solution by hevea brasilinesis sawdust activated carbon, Journal of Hazardous Materials, 124 (2005), 192-199. Google Scholar

[15]

S. M. H. MahmudW. ChenH. JahanY. LiuN. I. Sujan and S. Ahmed, IDTi–CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over–Sampling Technique SMOTE, IEEE Access, 7 (2019), 48699-48714. doi: 10.1109/ACCESS.2019.2910277. Google Scholar

[16]

D. O'Arroll, B. Sleep and M. Krol, et al., Nanoscale zero valent iron and bimetallic particles for contaminated site remediation, Advances in Water Resources, 51 (2013), 323–332.Google Scholar

[17]

H. OzakaiI. Sharma and W. Saktaywin, Performance of an ultral-low-pressure reverse osmosis membrane (UI PROM) for separating heavy metal: effects of interference parameters, Desalination, 144 (2002), 287-294. Google Scholar

[18]

C. D. Palmer and P. R. Wittrodt, Processes affecting the remediation of chromium-contaminated sites, Environ. Health Persp., 92 (1991), 25-40. Google Scholar

[19]

M. Sherman, J. G. Ponder, et al., Surface Chemistry and Electrochemistry of Supported Zerovalent Iron Nanoparticles in the Remediation of Aqueous Metal Contaminants, Chemistry of Materials, 13 (2001), 479–486.Google Scholar

[20]

I. B. Singh and D. R. Sing, Effects of pH on Cr-Fe interaction during cr(ⅵ) removal by metallic iron, Environment Technology, 24 (2003), 1041-1047. Google Scholar

[21]

T. Tosco, M. P. Papini And C. C. Viggi, et al., Nanoscale zero valent iron particles for groundwater remediation: A review, Journal of Cleaner Production, 77 (2014), 10–21.Google Scholar

[22]

C. UzumT. Shahwan and A. E. Eroglu, Application of zero-valent iron nanoparticles for the removal of aqueous co(2+) ions under various experimental conditions, Chemical Engineering Journal, 144 (2008), 213-220. Google Scholar

[23]

D. B. Vance, Nanoscale iron colloids: The maturation of the technology for field scale applications, Pollution Engineering, 37 (2005), 16-18. Google Scholar

[24]

P. Westerhoff and J. James, Nitrate removal in zero-valent iron packed columns, Water Research, 37 (2003), 1818-1830. Google Scholar

[25]

X. H. Zhang, J. Liu, H. T. Huang, et al., Chromium accumulation by the hyperaccumulator plant leersia hexandra swartz, Chemosphere, 67 (2007), 1138–1143.Google Scholar

show all references

References:
[1]

R. Benniceli, Z. Stpniewska, A. Banach, et al., The Ability of Azolla Caroliniana to Remove Heavy Metals (Hg(Ⅱ), Cr(Ⅲ), Cr(Ⅵ)) from Municipal Waste Water, Chemosphere, 55 (2004), 141–146.Google Scholar

[2]

S. Bosinco, J. Roussy, E. Guibal, et al., Interaction Mechanisms between Hexavalent Chromium and Corncob, Environmental Technology, 17 (1996), 55–62.Google Scholar

[3]

L. Breiman, Random forest, Machine Learning, 45 (2001), 5-32. Google Scholar

[4]

T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system[C]. Acm sigkdd international conference on knowledge discovery and data mining, Water Research, (2016), 785–794.Google Scholar

[5]

M. ChenQ. LiuS. ChenY. LiuC. Zhang and R. Liu, XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system, IEEE Access, 7 (2019), 13149-13158. doi: 10.1109/ACCESS.2019.2893448. Google Scholar

[6]

J. E. Cruver, Reverse Osmosis-Where it Stands Today, Water Sewage Works, 1973.Google Scholar

[7]

V. Dushenkov, P. B. A. N. Kumar, H. Motto, et al., Rhizofiltration: The use of plants to remove heavy metals from aqueous streams, Environmental Science & Technology, 29 (1995), 1239–1245.Google Scholar

[8]

I. C. Eromosele and S. S. Bayero, Adsorption of chromium and zinc ions from aqueous solutions by cellulosic graft copolymers, Bioresource Technology, 71 (2000), 279-281. Google Scholar

[9]

A. M. Farag, T. May, G. D. Marty, et al., The effect of chronic chromium exposure on the health of chinook salmon (Oncorhynchus Tshawytscha), Aquat Toxicol, 76 (2006), 246–257.Google Scholar

[10]

J. Farrell, J. P. Wang, P. O'Day, et al., Electrochemical and Spectroscopic Study of Arsenate Removal from Water Using Zero-Valent Iron Media, Environmental Science & Technology, 35 (2001), 2026–2032.Google Scholar

[11]

S. Gandhi, B. T. Oh, J. L. Schnoor, et al., Degradation of TCE, Cr(Ⅵ), Sulfate, and nitrate mixtures by granular iron in flow-through columns under different microbial conditions, Water Research, 36 (2002), 1973–1982.Google Scholar

[12]

C. R. gibbs, Characterization and application of ferrozine iron reagent as a ferrous iron indicator, Anal.Chem., 48 (1976), 1197-1200. Google Scholar

[13]

R. W. Gillham And S. F. O'Hannesin, Enhanced degradation of halogenated aliphatics by zero-valent iron, Ground Water, 32 (1994), 958.Google Scholar

[14]

T. KarthikeyanS. Rajgopal and L. R. Miranda, Chromium(Ⅵ) adsorption from aqueous solution by hevea brasilinesis sawdust activated carbon, Journal of Hazardous Materials, 124 (2005), 192-199. Google Scholar

[15]

S. M. H. MahmudW. ChenH. JahanY. LiuN. I. Sujan and S. Ahmed, IDTi–CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over–Sampling Technique SMOTE, IEEE Access, 7 (2019), 48699-48714. doi: 10.1109/ACCESS.2019.2910277. Google Scholar

[16]

D. O'Arroll, B. Sleep and M. Krol, et al., Nanoscale zero valent iron and bimetallic particles for contaminated site remediation, Advances in Water Resources, 51 (2013), 323–332.Google Scholar

[17]

H. OzakaiI. Sharma and W. Saktaywin, Performance of an ultral-low-pressure reverse osmosis membrane (UI PROM) for separating heavy metal: effects of interference parameters, Desalination, 144 (2002), 287-294. Google Scholar

[18]

C. D. Palmer and P. R. Wittrodt, Processes affecting the remediation of chromium-contaminated sites, Environ. Health Persp., 92 (1991), 25-40. Google Scholar

[19]

M. Sherman, J. G. Ponder, et al., Surface Chemistry and Electrochemistry of Supported Zerovalent Iron Nanoparticles in the Remediation of Aqueous Metal Contaminants, Chemistry of Materials, 13 (2001), 479–486.Google Scholar

[20]

I. B. Singh and D. R. Sing, Effects of pH on Cr-Fe interaction during cr(ⅵ) removal by metallic iron, Environment Technology, 24 (2003), 1041-1047. Google Scholar

[21]

T. Tosco, M. P. Papini And C. C. Viggi, et al., Nanoscale zero valent iron particles for groundwater remediation: A review, Journal of Cleaner Production, 77 (2014), 10–21.Google Scholar

[22]

C. UzumT. Shahwan and A. E. Eroglu, Application of zero-valent iron nanoparticles for the removal of aqueous co(2+) ions under various experimental conditions, Chemical Engineering Journal, 144 (2008), 213-220. Google Scholar

[23]

D. B. Vance, Nanoscale iron colloids: The maturation of the technology for field scale applications, Pollution Engineering, 37 (2005), 16-18. Google Scholar

[24]

P. Westerhoff and J. James, Nitrate removal in zero-valent iron packed columns, Water Research, 37 (2003), 1818-1830. Google Scholar

[25]

X. H. Zhang, J. Liu, H. T. Huang, et al., Chromium accumulation by the hyperaccumulator plant leersia hexandra swartz, Chemosphere, 67 (2007), 1138–1143.Google Scholar

Figure 1.  The relationship of pH-Eh of Cr(Ⅵ)
Figure 2.  Random forest based on decision trees
Figure 3.  The impact of the number of decision trees on XGBoost performance (coarse search)
Figure 4.  The impact of the number of decision trees on XGBoost performance (fine search)
Figure 5.  The impact of the max depth of decision trees on XGBoost performance
Figure 6.  The impact of the regular lambda of decision trees on XGBoost performance
Figure 7.  The results of XGBoost prediction
Figure 8.  The impact of the number of decision trees on the performance of random forest
Figure 9.  The impact of max depth of decision trees on the performance of random forest
Figure 10.  The results of random forest prediction
Table 1.  Experimental Setup
Experimental ParametersSetupUnits of measurement
Green tea extract content20, 30, 40, 50, 60mg/L
green tea extract / $ Fe^{2+} $1:3, 1:2, 1:1, 2:1, 3:1-
green tea extract preparation temperature40, 60, 80, 100
GT-Fe NPs synthesis temperature25, 35, 45, 55
pH value3, 5, 7, 9, 11-
dosage of GT-Fe NPs0.01, 0.02, 0.04, 0.06, 0.12g/L
Cr(Ⅵ) initial concentration40, 60, 80, 100, 160, 200mg/L
reaction temperature15, 25, 35, 45, 55
Experimental ParametersSetupUnits of measurement
Green tea extract content20, 30, 40, 50, 60mg/L
green tea extract / $ Fe^{2+} $1:3, 1:2, 1:1, 2:1, 3:1-
green tea extract preparation temperature40, 60, 80, 100
GT-Fe NPs synthesis temperature25, 35, 45, 55
pH value3, 5, 7, 9, 11-
dosage of GT-Fe NPs0.01, 0.02, 0.04, 0.06, 0.12g/L
Cr(Ⅵ) initial concentration40, 60, 80, 100, 160, 200mg/L
reaction temperature15, 25, 35, 45, 55
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