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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:

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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 ℃
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