WATER AND WASTEWATER
Year:2015 | Volume:25 | Issue:5 (93)|Papers Count:15

In this paper, the simultaneous removal of carbon and nutrients (nitrogen and phosphorus) from Faraman’s industrial wastewater (FIW) in a time-based sequencing batch reactor (SBR) was investigated. The experiments were conducted based on a central composite design (CCD) and analyzed using the response surface methodology (RSM). Reaction and aeration times were selected for the purposes of analyzing, modeling, and optimizing the process. Nine dependent parameters were monitored as process responses. The region of exploration for the process was taken as the area enclosed by the boundaries of reaction time (12-36 h) and aeration time (40-60 min/h). Reaction time was found to be the most effective variable and showed a decreasing impact on the total chemical oxygen demand (TCOD), slowly-biodegradable chemical oxygen demand (sbCOD), total nitrogen (TN), and total phosphorus (TP) removal efficiencies. The optimum operating conditions were determined in the range of 12 to 16 h for the reaction time and 40 to 60 min/h for the aeration time.

Heavy metals have been identified as major pollutants and also as one of the most serious environmental hazards. The aim of this study was to evaluate the efficacy of NF membrane to remove the heavy metals Pb, Cd, Cu and Cr+6 from waters containing sulfate. The membrane used in this study was of the NF, spiral-wounded model with a MWCO equal to 27Da, a cross-flowed stream, and a nominal capacity of 0.8 L/min. The effects of the parameters initial metal concentration (within the range of 5 to 50 mg/L for Pb, Cd, and Cu and 0.1 to 0.4mg/L for Cr+6 at four different concentrations), pH (4-9), flow rate (0.2, 0.4, 0.6, and 0.8 mg/L), and sulfate anion concentration (four concentrations ranging from 200 to 800 mg/L) on heavy metal removal efficiency of the membrane were investigated. Measurements were performed using atomic absorption and spectrophotometry. The maximum removal rates of Pb, Cd, Cu and Cr+6 for synthetic samples under optimal experimental conditions were 91, 97, 98, and 95%, and those for real samples were 72, 53, 87, and 99%, respectively. It was observed that the membrane efficiency for removing the three metals of Pb, Cd, and Cu decreased with increasing pH and initial metal concentration while it increased in the case of Cr+6. Increasing flow to 0.6 L/min led to a slight increase in membrane removal efficiency for all the four metals. Based on the results obtained, NF membranes are capable of removing a high percentage of heavy metals and a significant increase is observed in their heavy metal removal efficiency in the presence of sulfate ions and with increasing flow up to a certain level.

Management decisions whose environmental impacts affect directly or indirectly surface waters must of necessity be based on adequate knowledge and information when water quality zoning and a clear picture of river water quality are sought. Water quality zoning is based on pollution criteria that are identified on the basis of different water quality parameters drawn from historical data and the water uses in the region. The aggregate of the data and parameters involved make river water quality modeling a complex process. In this paper, the Principal Component Analysis (PCA) is used to reduce the water quality parameters involved in the identification of river water pollution criteria. The method keeps those components with more variances. The results show that the first component transfers 93.59% of the variation in the data, while the first two and the first six components explain 96.67% and 99.99% of the variations, respectively. Based on the criteria thus identified, the fuzzy clustering analysis is used in a second stage of the study to classify the river intervals. For this purpose, the fuzzy water quality data are provided to generate the fuzzy similarity matrix based on the fuzzy relations. Then, the stabilized matrix and the clustering diagram are created. Finally, the river intervals are classified into similar categories using the proper thresholds. The efficiency of the proposed method is evaluated by employing water quality data collected from the Zayandehrood River monitoring stations.

Given the increasing importance of surface water bodies as supply sources of drinking water and regarding the requirement for using different chemicals at various stages of water treatment processes, it is essential to investigate coagulant consumption in water treatment plants. Determination of the required dosage of coagulants used in the coagulation and flocculation unit is one of the most important decisions in water treatment operations. For this purpose, the jar test is generally used to determine the type and concentration of suitable coagulants in a water treatment plant. However, the test is rather time-consuming and unreliable due to the inaccurate results it yields. Instead, intelligent methods can be employed to overcome this shortcoming of the jar test. In this study, experimental data were collected over the period from 2011 to 2012 and further refined for study. Two non-linear models based on adaptive neuro-fuzzy inference system (ANFIS) and GMDH-type neural networks were then developed and experimental results were used to determine the optimum poly-aluminium chloride dosage for use at Guilan water treatment plant. The effects of input parameters including temperature, pH, turbidity, suspended solids, electrical conductivity, and color were investigated on coagulant dosage. The ANFIS model was found to outperform the GMDH model in predicting the required poly-aluminium chloride dosage.

PAHs are toxic compounds with carcinogenic effects on humans that are released into the environment by incomplete combustion of fossil fuels. Three methods are commonly employed for PAHs pollutant removal: physical, chemical, and biological. From among these, the biological method which typically contains microbial processes and transforms pollutants to nontoxic or less toxic substances is the most innocuous and effective solution. In this study, attempts were initially made to enrich, isolate, and purify indigenous bacteria from PAHs polluted soil. In the second stage, the PCR method was exploited to identify the bacteria that had the capability of growth and reproduction in polluted conditions. It was found that the degrading bacteria are component species of gram negative bacilli determined asShewanella, Pseudomonas, and Achromobacter. The results of the present study indicate that the bacteria have the best performance in PAHs removal from polluted environments.

This paper employs nonlinear programming (NLP), genetic algorithm (GA), and fixed length gene genetic programming (FLGGP) for the real-time operation of a three-reservoir system (Karoon4, Khersan1, and Karoon3 reservoirs) in which dependent and independent approaches are used to forecast the hydroelectric energy generated by the system. Unlike the forecast-independent approach, in the forecast-dependent approach, the value of release in each period depends on the reservoir in flow of the same period. Moreover, nonlinear decision rule (NLDR) curves are considered, and the total deficiency function as well as efficiency criteria are used to investigate the results of each procedure used. Finally, the performances of real-time operation of single- and three-reservoir systems are investigated and compared. Results indicate that the FLGGP gives the most efficient function for the extraction of reservoir operation rules in both the approaches examined. Comparison of the forecast-dependent and independent approaches revealed no significant differences. Therefore, the forecastindependent approach may be recommended for application in the extraction of reservoir operation rules due to its simplicity and ease of application.

Methyl tertiary-butyl ether (MTBE) is a gasoline additive used to enhance gasoline octane. It is highly soluble in water and is known for its carcinogenicity; hence its high risk of groundwater contamination. The present study was mainly designed to investigate MTBE effect on gasoline solubility in water, determine the associated COD value, and identify the kinetics of MTBE elimination from water by the stripping method. The results show that the kinetics of MTBE elimination from water can be expressed by a first order equation with the rate constants obtained as 0.0055 min-1 and 0.0074 min-1 at the two stirring rates of 100 rpm and 250 rpm, respectively. These rate constants are evidently high and are indicative of the effectiveness of the stripping method in MTBE removal from water. A linear function was established between COD reduction with respect to time at room temperature at low stirring rates (100 rpm) but an exponential function at high stiring rates (250 rpm). The solubility of gasoline with different additives (MTBE, ethanol, methanol) in water was also investigated. The results revealed that gasoline solubility decreased from 90 mg/l to 80mg/l and 69 mg/l, respectively, in the presence of ethanol and methanol. This is while the solubility of MTBE-containing gasoline in water rose from 90 mg/l to 778 mg/l, which shows an 8-fold increase in solubility in water.

There is nowadays a growing concern about nitrate pollution in groundwater resources and its adverse impacts on the public health. The present experimental study was conducted to evaluate the efficiency of the Fe° calcium alginate process in nitrate reduction. For this purpose, the effect of Fe° adsorbed on calcium alginate on nitrate oxidation was investigated at a pH range of 2 to10, a contact time of 10 to 90 min, nitrate concentrations of 50 to 300 mg/L and with calcium alginat concentrations of 0.5, 1, and 2 mg/L. Nitrate level in the effluent was measured using spectrophotometry. Results showed that a pH level of 3 and a contact time of 15 min were the optimal values in the Fenton process for nitrate removal. Under these conditions, nitrate removal efficiencies for FeIII, FeII, Feo, FeII/Feo/H2O2, and FeIII/Feo/H2O2 were 10.5, 27.6, 36.5, 62.3, and 74%, respectively, for a retention time of 90 min, an initial nitrate concentration of 100 mg/L, an iron concentration of 10 mg/L, and a pH level of 4. The results indicate that the corrective fenton process with zero iron nano-particles can effectively reduce nitrate under optimal conditions and that this method can be successfully used for the removal of similar compounds.

In this experiment, active sewage sludge was inoculated in organic waste. The objective was to study its effect on nutrient dynamics during vermicomposting. Active sewage sludge, as a source of nitrogen fixing and phosphorous solubilizing bacteria, was added in four combinations to the vermicomposting substrate. Prior to inoculation with active sludge, the treatments were precomposted for 30 days and finally vermicomposted for 40 days. Results showed that inoculation of microorganisms in the substrate accompanied by earthworms’ activity enhances the organic waste biodegradation rate. Increasing sludge concentration from 0 to 6000 mg/l led to reduced Total Organic Carbon from 32.76 to 29.91%, Total Volatile Solids from 49.85 to 48/02%, and C/N ratio from 19.59 to 16.06 but increased Total Kjeldahl Nitrogen from 1.68 to 1.87%, nitrate from 1476.75 to 1699.60 mg/kg, Total Phosphorous from 1.66 to 1.77 g/kg, and Electrical Conductivity from 3.10 to 3.48 mS/cm. By increasing the concentration of sewage sludge, heavy metals content also increased significantly due to the enhanced organic matter biodegradation. Finally, the results showed that, among the treatments, the one with an active sewage sludge concentration of 6000 mg/l had more desirable effects on the final vermicompost quality.Based on the reproducibility of the process and the quality of the final products, this experimental procedure may be proposed for studies requiring a mass reduction in the initial composted waste mixtures.

This study aims to develop a seasonal rainfall prediction model for the Aharchay Basin, northwest of Iran. The model is based on climate patterns of tele-connection including sea level pressure (SLP) and sea surface temperature (SST) over the period from 1965 to 2005. The models cover both wet (from December to May) and dry (from June to November) seasons. For this purpose, the climatic patterns affecting the climate of the northwest of Iran were initially determined. In the second stage of the study, the correlation coefficient analysis and the Gamma Test (GT) technique were used to select the best predictors and to determine the best combination of the variables. The results revealed that the gamma test model outperformed the other model in determining the required input variables and their best combination. The seasonal rainfall in the basin was also predicted using the Support Vector Machines (SVM) and the results thus obtained were compared with those of the multivariate linear regression model as a benchmark to show the performance of the SVM model in rainfall prediction.

A novel idea based on the Particle Swarm Optimization (PSO) is presented in this paper to minimize water-level fluctuations due to the sudden increase in downstream pumping discharge. The optimum input hydrograph which is capable of minimizing water surface fluctuations is obtained by using the Fourier Series and the PSO algorithm to determine the unknown coefficients in the Fourier Series. This idea can convert the problem to an optimization one, which can be solved via various optimization methods. To achieve this, a robust shockcapturing model which is able to solve governing equations of the unsteady, non-uniform flow is effectively combined with an optimization method based on the particle swarm optimization algorithm. The results show that the proposed approach is efficient in solving problems of this type. The inflow hydrograph obtained by this technique reduced water-level fluctuations in a much simpler manner compared to the complicated analytical approaches. Also, the proposed method was capable of reducing the fluctuations by 43.6% and 4.4%, respectively, compared to the case of imposing no control or that of control obtained by the variational approach.

Activated carbon is a valuable product that has many industrial and environmental applications and therefore it’s production from cheap wastes, is an economic-environmental action. According to the multiplicity of factors that affect adsorption properties of produced activated carbon, the design of experiments via Taguchi method was used to identify the main factors and optimization of activated carbon production. Based on the results, the impregnation ratio and activating reagent have the greatest impact on iodine number of almond shell activated carbon compared to other factors. In this study, activated carbon was produced with iodine number equal to 1331 mg/g, by optimization of chemical activation almond shell. Adsorption experiments showed that adsorption kinetics of dissolved petroleum hydrocarbons on activated carbon derived has followed from second-order reaction. Near to 95% of adsorption was happened during the first 30 minutes of process. Also the adsorption isotherm data were well followed from Freundlich isotherm model.

Lead poisoning is an important water quality parameter. The variety of adverse health effects caused by lead accumulation in the human body warrants the investigation of lead concentrations in drinking water. The presence of lead in drinking water in Iran is mostly due to pipes, fittings, brass or bronze water service connection valves, faucets, and fixtures, and other end use devices. For the purposes of this study, 10 samples of brass or bronze valves, as the major source of lead release in drinking water, were tested to determine the concentration of lead in water released from these devices. The same experiment was also carried out using 9 polypropylene valves recently introduced into the Iranian market. The results showed that lead release from brass or bronze valves was responsible for a major portion of drinking water lead concentrations that ranged from 7 to 700 times its maximum allowable limit for drinking water. In contrast, the amounts released from polypropylene valves into drinking water were found to be much less such that half the samples contained lead levels below the maximum allowable limit.

This study investigates the use of multi-criteria decision making in waste management and selection of the site location and proper wastewater collection and processing system in Varzeqan Town. Moreover, the effect of the decision-making method employed on ranking the final alternatives will be evaluated. Different factors are involved in the selection of wastewater treatment construction sites. These factors, in turn, not only depend on the special location and time the decision is made but also have their own weights in the final decision. In this study, the environmental conditions in the study area are initially investigated to select one site from among five different alternatives proposed for the construction of the facility using the AHP method. Also, the AHP method is used to evaluate and select the suitable collection and treatment method from among the four nonconventional options including Septic Tank Effluent Gravity, Septic Tank Effluent Pressure, Vacuum Sewerage System, and Simplified Sewerage for this region based on the special geographical and geotechnical conditions.The results indicate that the barren lands between Varzeghan Town and Dizaj Safar Ali Village are the best site for constructing the treatment facility and that the simplified sewarge system is the best collection system for the region.