Batch and column studies were performed to determine the effect of pH, empty bed contact time (EBCT), and hydraulic loading rate (HLR) on lead removal by granular activated carbon (GAC) columns. Lead removal increased with increasing pH, and for the majority of the adsorbate:adsorbent ratios investigated, was 100 % at pHs <

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Abstract. Investigation on the potential of sand filter as a pre-treatment of waste water was done in Kangar wet market, Perlis. Besides, the best composition of filter in order to treat wastewater based on BOD, COD, SS, AN, turbidity and pH levels are further examined. In this study, there are four types of sand filter composition which the medias consist of fine sand and coarse sand while the modified sand filter are consist of sand, course sand and activated carbon prepared from rice husk and coconut shells. After 10 weeks of treatment, the results show that the concentration of BOD, COD, SS, AN, turbidity and pH were reduced up to 86%, 84%, 63%, 88%, 73%, respectively while pH nearly to neutral with 6.83. Moreover, the result also revealed that the sand filter added with rice husk almost complied with Standard B of Malaysia Environmental Quality (Sewage) Regulations 2009 as well as gives the highest number of WQI with 36.81. Overall, WQI obtained in this study are ranged from 12.77 to 36.81.

A granular activated carbon (GAC) column is an effective treatment technology for the removal of lead. However, this technology requires time-consuming and expensive bench-and pilot-scale studies to design a full-scale system. A virtual adsorber system (VAS) based on artificial neural network technology was developed from 67 bench-scale experiments as a new tool to optimize the GAC process.In addition, VAS can be used to design a full-scale adsorber system by eliminating the need for further lengthy and costly experiments. Data obtained from the VAS indicated that decreasing the influent lead concentration from 50 to 1 ppm increased the number of bed volumes (BVs) of wastewater treated at breakthrough from 30 to 950 BVs and exhaustion from 200 to 1650 BVs, while the surface loading decreased from 17 to 1.8 g Pb/g carbon. In addition, increasing the empty bed contact time from 1.85 to 12.75 minutes for each influent lead concentration increased the bed volumes of wastewater treated at breakthrough, while the bed volumes at exhaustion decreased and the surface loading slightly changed for the lower Pb concentration (1 and 10 ppm of Pb). Five sets of training data were selected to test the VAS. It was found that the VAS could predict the bed volumes at breakthrough and exhaustion, and surface loading with an accuracy of 97%. The average coefficients of correlation, R , between actual and virtual bed volume measurements at breakthrough and exhaustion and for surface loading were 0.988, 0.980, and 0.988, respectively, for the verification data, while they were 0.996, 0.994, and 0.996 for the training data. The high values of the correlation coefficients demonstrated the high performance of the VAS for the removal of lead. 965