Figure 1.
The relationship of pH-Eh of Cr(Ⅵ)
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Triangular picture fuzzy linguistic induced ordered weighted aggregation operators and its application on decision making problems
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 |
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.
Mathematics Subject Classification:
Primary: 92E99.
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
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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. |
| [2] |
S. Bosinco, J. Roussy, E. Guibal, et al., Interaction Mechanisms between Hexavalent
Chromium and Corncob, Environmental Technology, 17 (1996), 55–62. |
| [3] |
L. Breiman,
Random forest, Machine Learning, 45 (2001), 5-32.
|
| [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. |
| [5] |
M. Chen, Q. Liu, S. Chen, Y. Liu, C. 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. |
| [6] |
J. E. Cruver, Reverse Osmosis-Where it Stands Today, Water Sewage Works, 1973. |
| [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. |
| [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.
|
| [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. |
| [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. |
| [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. |
| [12] |
C. R. gibbs,
Characterization and application of ferrozine iron reagent as a ferrous iron indicator, Anal.Chem., 48 (1976), 1197-1200.
|
| [13] |
R. W. Gillham And S. F. O'Hannesin, Enhanced degradation of halogenated aliphatics by zero-valent iron, Ground Water, 32 (1994), 958. |
| [14] |
T. Karthikeyan, S. 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.
|
| [15] |
S. M. H. Mahmud, W. Chen, H. Jahan, Y. Liu, N. 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. |
| [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. |
| [17] |
H. Ozakai, I. 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.
|
| [18] |
C. D. Palmer and P. R. Wittrodt,
Processes affecting the remediation of chromium-contaminated sites, Environ. Health Persp., 92 (1991), 25-40.
|
| [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. |
| [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.
|
| [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. |
| [22] |
C. Uzum, T. 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.
|
| [23] |
D. B. Vance,
Nanoscale iron colloids: The maturation of the technology for field scale applications, Pollution Engineering, 37 (2005), 16-18.
|
| [24] |
P. Westerhoff and J. James,
Nitrate removal in zero-valent iron packed columns, Water Research, 37 (2003), 1818-1830.
|
| [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. |
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. |
| [2] |
S. Bosinco, J. Roussy, E. Guibal, et al., Interaction Mechanisms between Hexavalent
Chromium and Corncob, Environmental Technology, 17 (1996), 55–62. |
| [3] |
L. Breiman,
Random forest, Machine Learning, 45 (2001), 5-32.
|
| [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. |
| [5] |
M. Chen, Q. Liu, S. Chen, Y. Liu, C. 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. |
| [6] |
J. E. Cruver, Reverse Osmosis-Where it Stands Today, Water Sewage Works, 1973. |
| [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. |
| [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.
|
| [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. |
| [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. |
| [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. |
| [12] |
C. R. gibbs,
Characterization and application of ferrozine iron reagent as a ferrous iron indicator, Anal.Chem., 48 (1976), 1197-1200.
|
| [13] |
R. W. Gillham And S. F. O'Hannesin, Enhanced degradation of halogenated aliphatics by zero-valent iron, Ground Water, 32 (1994), 958. |
| [14] |
T. Karthikeyan, S. 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.
|
| [15] |
S. M. H. Mahmud, W. Chen, H. Jahan, Y. Liu, N. 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. |
| [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. |
| [17] |
H. Ozakai, I. 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.
|
| [18] |
C. D. Palmer and P. R. Wittrodt,
Processes affecting the remediation of chromium-contaminated sites, Environ. Health Persp., 92 (1991), 25-40.
|
| [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. |
| [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.
|
| [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. |
| [22] |
C. Uzum, T. 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.
|
| [23] |
D. B. Vance,
Nanoscale iron colloids: The maturation of the technology for field scale applications, Pollution Engineering, 37 (2005), 16-18.
|
| [24] |
P. Westerhoff and J. James,
Nitrate removal in zero-valent iron packed columns, Water Research, 37 (2003), 1818-1830.
|
| [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. |
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 Parameters | Setup | Units of measurement |
| Green tea extract content | 20, 30, 40, 50, 60 | mg/L |
| green tea extract / | 1:3, 1:2, 1:1, 2:1, 3:1 | - |
| green tea extract preparation temperature | 40, 60, 80, 100 | ℃ |
| GT-Fe NPs synthesis temperature | 25, 35, 45, 55 | ℃ |
| pH value | 3, 5, 7, 9, 11 | - |
| dosage of GT-Fe NPs | 0.01, 0.02, 0.04, 0.06, 0.12 | g/L |
| Cr(Ⅵ) initial concentration | 40, 60, 80, 100, 160, 200 | mg/L |
| reaction temperature | 15, 25, 35, 45, 55 | ℃ |
| Experimental Parameters | Setup | Units of measurement |
| Green tea extract content | 20, 30, 40, 50, 60 | mg/L |
| green tea extract / | 1:3, 1:2, 1:1, 2:1, 3:1 | - |
| green tea extract preparation temperature | 40, 60, 80, 100 | ℃ |
| GT-Fe NPs synthesis temperature | 25, 35, 45, 55 | ℃ |
| pH value | 3, 5, 7, 9, 11 | - |
| dosage of GT-Fe NPs | 0.01, 0.02, 0.04, 0.06, 0.12 | g/L |
| Cr(Ⅵ) initial concentration | 40, 60, 80, 100, 160, 200 | mg/L |
| reaction temperature | 15, 25, 35, 45, 55 | ℃ |
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