Polynomial Regression Analysis for Removal of Heavy Metal Mixtures in Coagulation/Flocculation of Electroplating Wastewater


Siti Wahidah Puasa(1*), Kamariah Noor Ismail(2), Muhammad Amarul Aliff Mahadi(3), Nur Ain Zainuddin(4), Mohd Nazmi Mohd Mukelas(5)

(1) Faculty of Chemical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
(2) Faculty of Chemical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
(3) Faculty of Chemical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
(4) Faculty of Chemical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
(5) Faculty of Chemical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
(*) Corresponding Author


Wastewater produced from the electroplating industry generally consists of heavy metals mixture and organic materials that need to be treated before it can be discharged to the environment. Thus, the present investigation was focused on the selectivity removal of heavy metal mixtures consists of Copper (Cu), Cadmium (Cd), and Zinc (Zn). Several operating conditions, including the effect of pH and coagulant (FeCl3) dosage, were varied to find the best performance of heavy metal removal. Results show the efficiency of heavy metals removal for both wastewater characteristics were approximately 99%. The experimental data on the treatment of synthetic wastewater was plotted using polynomial regression (PR) via Excel software. The value of adjusted R2 obtained for the final concentration of Cu, Zn, and Cd after treatment were 0.6884, 0.9676, and 0.9283, respectively, which shows data were acceptably fitted for Cu and very well fitted for Zn and Cd. The coagulation/flocculation process performed on actual wastewater shows that the lowest final concentration of Cu, Zn, and Cd after treatment were 0.487, 1.232, and 0 mg/L respectively at pH of 12.


hydroxide precipitation; metal removal; coagulation-flocculation; electroplating wastewater; polynomial regression

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DOI: https://doi.org/10.22146/ijc.52251

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