Figure 1.
The relationship of pH-Eh of Cr(Ⅵ)
-
Previous Article
Using distribution analysis for parameter selection in repstream
- MFC Home
- This Issue
-
Next Article
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
![]()
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. 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. 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. 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. Google Scholar |
| [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. Google Scholar |
| [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. 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. 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. 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. 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. 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. 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. Google Scholar |
| [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. Google Scholar |
| [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. 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. 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. 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 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 | ℃ |
| [1] |
Jian-Xin Guo, Xing-Long Qu. Robust control in green production management. Journal of Industrial & Management Optimization, 2020 doi: 10.3934/jimo.2021011 |
| [2] |
Kiyoshi Igusa, Gordana Todorov. Picture groups and maximal green sequences. Electronic Research Archive, 2021, 29 (5) : 3031-3068. doi: 10.3934/era.2021025 |
| [3] |
Shuhua Zhang, Zhuo Yang, Song Wang. Design of green bonds by double-barrier options. Discrete & Continuous Dynamical Systems - S, 2020, 13 (6) : 1867-1882. doi: 10.3934/dcdss.2020110 |
| [4] |
Kyoungsun Kim, Gen Nakamura, Mourad Sini. The Green function of the interior transmission problem and its applications. Inverse Problems & Imaging, 2012, 6 (3) : 487-521. doi: 10.3934/ipi.2012.6.487 |
| [5] |
Jongkeun Choi, Ki-Ahm Lee. The Green function for the Stokes system with measurable coefficients. Communications on Pure & Applied Analysis, 2017, 16 (6) : 1989-2022. doi: 10.3934/cpaa.2017098 |
| [6] |
Peter Bella, Arianna Giunti. Green's function for elliptic systems: Moment bounds. Networks & Heterogeneous Media, 2018, 13 (1) : 155-176. doi: 10.3934/nhm.2018007 |
| [7] |
Virginia Agostiniani, Rolando Magnanini. Symmetries in an overdetermined problem for the Green's function. Discrete & Continuous Dynamical Systems - S, 2011, 4 (4) : 791-800. doi: 10.3934/dcdss.2011.4.791 |
| [8] |
Sungwon Cho. Alternative proof for the existence of Green's function. Communications on Pure & Applied Analysis, 2011, 10 (4) : 1307-1314. doi: 10.3934/cpaa.2011.10.1307 |
| [9] |
Gang Xie, Wuyi Yue, Shouyang Wang. Optimal selection of cleaner products in a green supply chain with risk aversion. Journal of Industrial & Management Optimization, 2015, 11 (2) : 515-528. doi: 10.3934/jimo.2015.11.515 |
| [10] |
Van Hoang Nguyen. The Hardy–Moser–Trudinger inequality via the transplantation of Green functions. Communications on Pure & Applied Analysis, 2020, 19 (7) : 3559-3574. doi: 10.3934/cpaa.2020155 |
| [11] |
Chiu-Ya Lan, Huey-Er Lin, Shih-Hsien Yu. The Green's functions for the Broadwell Model in a half space problem. Networks & Heterogeneous Media, 2006, 1 (1) : 167-183. doi: 10.3934/nhm.2006.1.167 |
| [12] |
Ashkan Mohsenzadeh Ledari, Alireza Arshadi Khamseh, Mohammad Mohammadi. A three echelon revenue oriented green supply chain network design. Numerical Algebra, Control & Optimization, 2018, 8 (2) : 157-168. doi: 10.3934/naco.2018009 |
| [13] |
Lijun Zhang, Yixia Shi, Maoan Han. Smooth and singular traveling wave solutions for the Serre-Green-Naghdi equations. Discrete & Continuous Dynamical Systems - S, 2020, 13 (10) : 2917-2926. doi: 10.3934/dcdss.2020217 |
| [14] |
Lei Qiao. Asymptotic behaviors of Green-Sch potentials at infinity and its applications. Discrete & Continuous Dynamical Systems - B, 2017, 22 (6) : 2321-2338. doi: 10.3934/dcdsb.2017099 |
| [15] |
Zhi-Min Chen. Straightforward approximation of the translating and pulsating free surface Green function. Discrete & Continuous Dynamical Systems - B, 2014, 19 (9) : 2767-2783. doi: 10.3934/dcdsb.2014.19.2767 |
| [16] |
Kohei Ueno. Weighted Green functions of nondegenerate polynomial skew products on $\mathbb{C}^2$. Discrete & Continuous Dynamical Systems, 2011, 31 (3) : 985-996. doi: 10.3934/dcds.2011.31.985 |
| [17] |
Ivan C. Christov. Nonlinear acoustics and shock formation in lossless barotropic Green--Naghdi fluids. Evolution Equations & Control Theory, 2016, 5 (3) : 349-365. doi: 10.3934/eect.2016008 |
| [18] |
Claudio Giorgi, Diego Grandi, Vittorino Pata. On the Green-Naghdi Type III heat conduction model. Discrete & Continuous Dynamical Systems - B, 2014, 19 (7) : 2133-2143. doi: 10.3934/dcdsb.2014.19.2133 |
| [19] |
Kohei Ueno. Weighted Green functions of polynomial skew products on $\mathbb{C}^2$. Discrete & Continuous Dynamical Systems, 2014, 34 (5) : 2283-2305. doi: 10.3934/dcds.2014.34.2283 |
| [20] |
Claudia Bucur. Some observations on the Green function for the ball in the fractional Laplace framework. Communications on Pure & Applied Analysis, 2016, 15 (2) : 657-699. doi: 10.3934/cpaa.2016.15.657 |
Impact Factor:
Tools
Metrics
Other articles
by authors
[Back to Top]