Sequential Integration of Electrocoagulation and Nano composite for Treatment of Surface Water for Turbidity and Organic Acid Removal

Abstract

Surface water is contaminated by various types of pollutants like colloids, dissolved organic matters, and microbes. Occasionally, in conventional surface water treatment, pre-chlorination is done to reduce pathogenic load which may produce carcinogenic disinfection by-product (DBPs) due to presence of natural organic matter (NOM). newlineWith above context this work comprises synthesis ofFunctionalized Graphene Oxide (fRGO) to remove NOM from EC pretreated synthetic turbid water and the factors relatedto the batch mode were then modeled by Doehlert Design of Experiment and was optimized. newlineAt optimum pH (7.04) for EC 97.2% turbidity removal was achieved. At an equivalentAlum dose during chemical coagulation (CC), optimum pH and turbidity removal was 6.84 and 86.23% respectively. EC and CC are not capable of removal of NOM. So, powdered activated carbon was tested to remove NOM and only 49.50%removal was achieved (adsorption capacity 0.124 mg/g). With these shortcomings it was further felt necessary that an effective and low-cost adsorbent was required to develop for removal of NOM before disinfection.The results revealed thatcontact time, dose of adsorbent (fRGO), temperature and agitation speed have positive effect on NOM removalwhereaspH has negative effect. The NOM adsorption capacity offRGO and PAC was found to be 222.22 mg/g 6.826 mg/g respectively. The effect of the factors on NOM removal studied above was modeled and optimized. The model developedhas coefficient of regression, R2=0.9931 and maximum error in predicting NOM removal was found to be+2.42%. At optimum condition (pH=6.882, time =4.27 hrs.,fRGO=0.016 g/L, temperature=302 K, agitation speed=165 rpm)98.19% of NOM removal was achieved. newlineThe column breakthrough study followed the Clark model of adsorption and the maximum error obtained was calculated using Marquardt s percent standard deviation (MPSED)and was found to be less than 10%.The Bed Depth Service Time (BDST) model was tested and considering breakthrough at 0.1% and exhaustion at 99% of influent