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Data modeling analysis on removal efficiency of hexavalent chromium

  • * Corresponding author: Dong Li

    * Corresponding author: Dong Li 
Abstract Full Text(HTML) Figure(10) / Table(1) Related Papers Cited by
  • 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.


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  • 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
     | Show Table
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