Removal of high concentration of sulfate from pigment industry effluent by chemical precipitation using barium chloride: RSM and ANN modeling approach.

Title Removal of high concentration of sulfate from pigment industry effluent by chemical precipitation using barium chloride: RSM and ANN modeling approach.
Authors N. Kartic; C.Aditya Narayana; M. Arivazhagan
Journal J Environ Manage
DOI 10.1016/j.jenvman.2017.10.017
Abstract

Sulfate ions pose a major threat and challenge in the treatment of industrial effluents. The sample of wastewater obtained from a pigment industry contained large quantities of sulfate in the form of sodium sulfate which resulted in high TDS. As the removal of sulfate from pigment industry effluent was not reported previously, this work was focused on removing the sulfate ions from the effluent by chemical precipitation using barium chloride. The efficiency of sulfate removal was nearly 100% at an excess dosage of barium chloride, which precipitates the dissolved sulfate ions in the form of barium sulfate. Optimization of the parameters was done using Response Surface Methodology (RSM). This work is the first attempt for modeling the removal of sulfate from pigment industry effluent using RSM and Artificial Neural Network (ANN). Prediction by both the models was evaluated and both of them exhibited good performance (R2 value > 0.99). It was observed that the prediction by RSM (R2 value 0.9986) was closer to the experimental results than ANN prediction (R2 value 0.9955). The influence on the pH and conductivity of the solution by dosage of precipitant was also studied. The formation of barium sulfate was confirmed by characterization of the precipitate. Therefore, the sulfate removed from the effluent was converted into a commercially valuable precipitate.

Citation N. Kartic; C.Aditya Narayana; M. Arivazhagan.Removal of high concentration of sulfate from pigment industry effluent by chemical precipitation using barium chloride: RSM and ANN modeling approach.. J Environ Manage. 2018;206:6976. doi:10.1016/j.jenvman.2017.10.017

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