ABSTRACT
The problem of removing pollutants from water is an important process and is becoming more important with the increasing of industrial activities. One of the most important environmental problems is water resources pollution by Heavy metals. Bioabsorbtion is affordable adsorbents for the removal of heavy metals from aqueous solutions. This study explores the adsorption potential of Cr [VI], Cd [II] and Ni [II] by Elaeagnus angustifolia fruit powder in aqueous solution. The influences of several main parameters such as pH value of solution, sorbent amount, initial metal ion concentration, and contact time in batch experiments have been studied. The adsorption was depended on pH, adsorbent dosage, and contact time. Maximum removal of Cr [VI], Cd [II] and Ni[II] were obtained 7.19mg/g, 2.42 mg/g and 1.97 mg/g respectively. Adsorption isotherms were compared with both Langmuir and Freundlich adsorption models. The experimental data best fit with the Langmuir isotherm model. Experimental results show natural biosorbent was effective for the removal of pollutants from aqueous solution
ABSTRACT
Fuzzylogic computes a multi-criteria evaluation by means of either a Boolean analysis, Weighted Linear Combination [WLC] and Ordered Weighted Averaging [OWA] of factor images. OWA works with standardized factor images and employs a variant of the WLC. It takes into account the risk associated with the decision and degree of tradeoff associated with the variables in the analysis. In this research, for Ecological Capability Assessment and watersheds management in study area, we have studied 22 biological and physiological factors. For ecological capability evaluation, the method of OWA was deployed. This method involves criterion weights and order weights. The generality of OWA is related to its capability to implement different combination operators by selecting appropriate order weights. By specifying suitable order weights, it is possible to change the form of aggregation from the minimum-type combination through all intermediate types including the conventional weighted linear combination, to the maximum-type combination. The paper focuses on the OWA method as well as an approach for integrating Geographic Information System [GIS] and OWA. OWA has been developed as a generalization of multi-criteria combination. The OWA concept has been extended to the GIS applications as part of a decision support module in GIS. In this study to obtain the criteria weights, comparisons were made by evaluating 22 criteria against each other, therefore we attained comparable data via the technique of Analytical Hierarchy Process [AHP] and five scenarios of OWA method were used. The results of field studies, third scenario for the study area proposed