PERFORMANCE OF A VIRTUAL ADSORBER SYSTEM FOR REMOVAL OF LEAD

A granular activated carbon (GAG) column is an effective treatment tec hnology for the removal of lead. However, this technology requires tim e-consuming and expensive bench- and pilot-scale studies to design a f ull-scale system. A virtual adsorber system (VAS) based on artificial neural network technology was developed from 67 bench-scale experiment s as a new tool to optimize the GAC process. In addition, VAS can be u sed to design a full-scale adsorber system by eliminating the need for further lengthy and costly experiments. Data obtained from the VAS in dicated that decreasing the influent lead concentration from 50 to 1 p pm increased the number of bed volumes (BVs) of wastewater treated at breakthrough from 30 to 950 BVs and exhaustion from 200 to 1650 BVs, w hile the surface loading decreased from 17 to 1.8 g Pb/g carbon. In ad dition, increasing the empty bed contact time from 1.85 to 12.75 minut es for each influent lead concentration increased the bed volumes of w astewater treated at breakthrough, while the bed volumes at exhaustion decreased and the surface loading slightly changed for the lower Pb c oncentration (1 and 10 ppm of Pb). Five sets of training data were sel ected to test the VAS. It was found that the VAS could predict the bed volumes at breakthrough and exhaustion, and surface loading with an a ccuracy of 97%. The average coefficients of correlation, R, between ac tual and virtual bed volume measurements at breakthrough and exhaustio n 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 de monstrated the high performance of the VAS for the removal of lead.