Industrial effluents are usually one of the major industries polluting the environment and surface water. It is estimated that the worldwide production of dyes is about 70 tons/year. To overcome this problem, innovative processes are suggested for the treatment of industrial effluents containing dyes and heavy metals. The goal of the processes is often to reduce the toxicity of these pollutants in order to meet treatment standards. Recently, great attention has been paid to innovative processes for physical and chemical removal techniques such as adsorption on new adsorbents, biomass adsorption, membrane filtration, irradiation, and electrochemical coagulation. In this study, the application of adsorbents in the adsorption process to remove dye pollutants from industrial effluents has been studied. Factors affecting dye adsorption such as pH, temperature, initial dye concentration, and adsorbent amount are also presented. The obtained results revealed that more than 80% of the dye adsorption on the surface of adsorbents are endothermic processes and more than 95% of the processes obey the pseudo-second-order kinetic model.
The suggestions system is a part of total quality management to create individual and group spirit of partnership between staff and increase efficiency in the organization. Also, diagnosis and improvement process is one of the steps of the chain in the processes of suggestions system. In this study, an approach has been proposed to evaluate efficiency of organizations in performing suggestions system with these aims: (1) Reviewing all the elements in the successful implementation of the suggestions system and (2) providing an effective scientific approach to evaluate the organizations on implementing this system considering the uncertainty in the data. Methodology used in this study included the following techniques: (1) Factor analyzing to clarify the internal correlation between significant criteria and detect the major criteria and (2) using robust data envelopment analysis (RDEA) model to evaluate efficiency of organizations in performing suggestions system. The method is based on 3 inputs and 17 outputs in which some outputs are uncertain scores in form of intervals with uncertain bounds. This model has been solved for different Gs, and a value of weights and rankings for each Decision Making Unit (DMU) has been saved by using the obtained values. In the following a simulation has been used to compute the conformity of the rankings from the RDEA model with reality. Doing so shows that the maximum conformity occurs G = 6. Therefore, we can conclude that specific values of G can maximize conformity and thus more authentic final rankings for the DMUs in this interval of G may be expected.
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