# American Institute of Mathematical Sciences

• 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

* 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:

show all references

##### References:
The relationship of pH-Eh of Cr(Ⅵ)
Random forest based on decision trees
The impact of the number of decision trees on XGBoost performance (coarse search)
The impact of the number of decision trees on XGBoost performance (fine search)
The impact of the max depth of decision trees on XGBoost performance
The impact of the regular lambda of decision trees on XGBoost performance
The results of XGBoost prediction
The impact of the number of decision trees on the performance of random forest
The impact of max depth of decision trees on the performance of random forest
The results of random forest prediction
Experimental Setup
 Experimental Parameters Setup Units of measurement Green tea extract content 20, 30, 40, 50, 60 mg/L green tea extract / $Fe^{2+}$ 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 / $Fe^{2+}$ 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: