Electrocoagulation process for the treatment of metal-plating wastewater: Kinetic modeling and energy consumption

The wastewater from industrial area was treated by EC via Fe and Al electrodes. Cu, Ni, Cr and Zn were highly removed at the first minutes, simultaneously. Pseudo-2nd-order was found to be more suitable for kinetics. Adsorption capacities based on kinetic modeling were observed as Cr>Cu>Ni>Zn. The chemical cost in the case of pH adjustment after EC was less as 3.83 $/m³. It is known that wastewater produced by the metal-plating industry contains several heavy metals, which are acidic in nature and therefore toxic for the environment and for living creatures. In particular, heavy metals enter the food chain and accumulate in vital organs and cause serious illness. The precipitation of these metals is mostly achieved by pH adjustment, but as an alternative to this method, the electrocoagulation process has investigated in this study using iron and aluminum electrodes. The effects of the pH adjustment on removal before and after the electrocoagulation process were investigated, and cost analyses were also compared. It was observed that a high proportion of removal was obtained during the first minutes of the electrocoagulation process; thus, the current density did not have a great effect. In addition, the pH adjustment after the electrocoagulation process using iron electrodes, which are 10% more effective than aluminum electrodes, was found to be much more efficient than before the electrocoagulation process. In the process where kinetic modeling was applied, it was observed that the heavy metal removal mechanism was not solely due to the collapse of heavy metals at high pH values, and with this modeling, it was seen that this mechanism involved adsorption by iron and aluminum hydroxides formed during the electrocoagulation process. When comparing the ability of heavy metals to be adsorbed, the sequence was observed to be Cr>Cu>Ni>Zn, respectively.     

On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Received: January 2004 / Revised: July 2005