wastewater

The present work is aimed to examine the elimination of cyanide ions from the wastewater derived from the Agh-Darreh gold mine using the Caro’s acid method. The response surface modeling is utilized to evaluate and optimize the influential parameters such as the sulfuric acid/hydrogen peroxide ratio, pH, Caro’s acid concentration, and contact time on the elimination process. The results obtained indicate that the increase in the Caro’s acid concentration and contact time has a positive impact on the elimination of the free cyanide ions, while the increment in the weight ratio of sulfuric acid/hydrogen peroxide and pH higher than 9.5 demonstrate a negative impact. Also it is found that the quadratic effect of pH has the highest influence on the removal of cyanide ion, and the linear effect of the ratio of sulfuric acid/hydrogen peroxide has the lowest degree of importance. Additionally, the optimization process is carried out, and about 96.4% of the cyanide ions is eliminated from the wastewater under the optimal conditions including 2 g/L Caro’s acid concentration, 9.3 pH, 8 min contact time, and sulfuric acid to hydrogen peroxide (weight) ratio of 2.

In this work, two clay-based composites are prepared for the adsorptive removal of the chloride ions from aqueous solutions. These composites are characterized through Fourier transform-infrared spectroscopy, scanning electron microscopy, X-ray fluorescence spectroscopy, and X-ray diffraction analysis. The effects of different parameters such as the contact time, amount of adsorbent, chloride concentration, temperature, and pH are studied by batch experiments. Also the isotherm, kinetic, and thermodynamic of the adsorptive removal of the chloride ions from these two composites are investigated. According to the results obtained, the adsorptive removal of chloride ions is initially rapid, and the equilibrium time is reached after 30 min. The optimal pH value is 7.0 for a better adsorption, and the maximum capacity can be achieved, which is 60.2 mg/g with 1000 mg/L of the initial chloride concentration. The Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich adsorption models are applied to describe the equilibrium isotherms at different chloride concentrations. According to the equilibrium isotherms and the correlation coefficients (R2CDC: 0.9424, R2LDC: 0.996), the process can be described by the Langmuir model, and exhibits the highest removal rate of 97.24% (24.31 mg/g) with 250 mg/L of the initial chloride concentration. The pseudo-first-order and pseudo-second-order, intra-particle diffusion, and mass transfer kinetics models are used to identify the mechanism of the adsorptive removal of the chloride ions. The pseudo-second order model due the correlation coefficients (R2CDC: 0.9217-0.9852, R2LDC: 0.9227-0.9926) can be fitted to the kinetic calculations, and it is applicable for the adsorptive removal of chloride ions by the adsorbents. The thermodynamic calculations show that in a low chloride concentration, the sorption is spontaneous, associative, and endothermic; and in a high concentration, it is unspontaneous, dissociative, and endothermic. The calculated value of free energy (E) for adsorption onto the adsorbents suggests that the reaction rate controls the adsorptive removal of the chloride process rather than diffusion. It can be concluded that these two composites can be used as effective and applicable adsorbents for the adsorptive removal of chloride ions.

The Mighan playa/lake is characterized as a closed catchment. In the recent years, the rapid industrialization and urbanization has resulted in a pollution area in the city of Arak. In this work, we focus on six regions around the playa/lake to study the distribution of heavy metals in the waters and their contamination risk. A total of 32 water samples are analyzed to determine the contamination degree of heavy metals, i.e. Hg, As, Cd, Cr, Cu, Pb, and Zn. The heavy metal pollution index, heavy metal evaluation index, and degree of contamination are utilized to assess the pollution extent of these metals. The spatial distribution patterns reveal that the waters in different areas of playa/lake are in a good condition. The island, lake in playa, and the Wastewater Mineral Salts Company are most seriously polluted with Pb, being higher than the standard of drinking water quality limit. Water in the wastewater treatment plant is polluted with Hg and As. The correlation matrix, factor analysis, and cluster analysis are used to support the idea that Pb may be mainly derived from the atmospheric input, and As and Hg from the wastewater treatment plant, agricultural lands, and domestic waste. Many native and migratory birds live in the Mighan playa, which is exposed to heavy metals. Therefore, it is required to monitor heavy metals in the Arak playa and to manage the municipal, industrial, and agricultural activities around it and to reduce them.

The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.

Two common waste materials, red mud and fly ash, were used to produce a new nano-hybrid adsorbent by heat treatment with alkali addition. The new zeolitic structure formation of the hybrid adsorbent was revealed using the BET surface area, XRD, and SEM analyses. This hybrid adsorbent was utilized to remove arsenic from synthetic and real waste waters by batch and column adsorption experiments. The parameters such as the pH, contact time, and effect of the co-existing ions were investigated. Slightly acidic media favored arsenic adsorption by the hybrid adsorbent, the same as the individual use of fly ash and red mud. The effects of ions such as Fe3, Cu2, Cl-, SO42-, and PO43- were investigated as the co-existing ions. It was found that arsenic adsorption increased with cationic ions and decreased with anionic ions according to their valance charge. The intra-particle diffusion model showed that adsorption took place at three different rates depending on time. The hybrid adsorbent was formed as a pellet and utilized in a column for treatment of arsenic containing real waste water. The hybrid adsorbent derived from mineral wastes was more successful than their individual usages.

Arsenic is one of the heavy metals and nearly all its compounds, especially organic compounds, are toxic. The wide spectrum of diseases caused by this element has led to evaluation of the toxicity of different arsenic species and identification of the major natural and anthropogenic pollution sources of it in the nature. Mining activities are among the main sources of anthropogenic pollution of soil and water by arsenic. The purpose of this study was geochemical modeling of different arsenic species in the wastewater of the tailings dam of Mouteh Gold processing plant in Iran to evaluate the effect of pH and temperature on the stability of these components. Modeling was done using MINTEQ software. The results showed that arsenic species at different pH values under study were H3AsO3, H2AsO3- and HAsO32-, and their actual concentration in the plant wastewater were negligible. MINTEQ software introduced H3AsO4, H2AsO4-, HAsO42- and AsO43- as arsenic V species at different pH values, of which HAsO42- and AsO43- were the main components of arsenic in plant wastewater. Given the low toxicity of arsenic V species and their easier elimination relative to arsenic III species, in the current conditions, the plant wastewater is in a good status in terms of arsenic pollution. Also temperature changes have little effect on the concentration of various arsenic species in the wastewater.