WATER AND WASTEWATER
Year:2011 | Volume:21 | Issue:4 (76)|Papers Count:15

In this paper, Analytical Hierarchy Process (AHP) method that is based on expert knowledge is used for the selection of the optimal anaerobic wastewater treatment process in industrial estates. This method can be applied for complicated multi-criteria decision making to obtain reasonable results. The different anaerobic processes employed in Iranian industrial estates consist of UASB, UAFB, ABR, Contact process, and Anaerobic Lagoons. Based on the general conditions in wastewater treatment plants in industrial estates and on expert judgments and using technical, economic, environmental, and administrative criteria, the processes are weighted and the results obtained are assessed using the Expert Choice Software. Finally, the five processes investigated are ranked as 1 to 5 in a descending order of UAFB, ABR, UASB, Anaerobic Lagoon, and Contact Process. Sensitivity analysis showing the effects of input parameters on changes in the results was applied for technical, economic, environmental, and administrative criteria.

Corrosion is one of the main problems of sewers around the world which causes huge quantities of both economic and ecological damages. Biological corrosion accounts for 20% of the total microbiological corrosion worldwide. The purpose of this study was to determine the corrosion potential in Ray sewers by H2S and ORP borne by wastewater. In this survey, H2S and Oxidation-Reduction Potential (ORP) were measured using a portable H2S meter (MICRO III mark, model G203s) and a portable ORPmeter (mark Hana), respectively. The results were subsequently compared with those of CCTV (model IBAK) visual observations. The mean values of H2S in the sewers were found to be 0.83 ppm with a standard deviation of 0.931 at 9 a.m. and 1 ppm with a standard deviation of 1.155 at 3 p.m. Also, the mean values of ORP were -8.42 mV with a standarddeviation of 60.43 at 9 a.m. and -37.3 mV with a standard deviation of 66.85 at 3 p.m. Based on the amounts of H2S and ORP produced in Ray sewers and also the results obtained from CCTV visual observations, the corrosion potential of wastewater in these sewers was judged to be considerably high.

Aniline is a first type amino aromatic compound and has various applications in different pharmaceutical, synthetic dye, plastic, and petrochemical industries. It is poisonous and its discharge into the environment causes serious hazards that warrant it removal by an efficient treatment process. In this study, the efficiency of rotating biological contactors in aniline removal was investigated using four 3-liter parallel systems (in two series). Two reactors in the first series had 27 disks. The second series had 14 discs with packings in each reactor with the same specific surfaces as compared to the first system. Aniline concentrations from 100 to 1200 mg/L and hydraulic loading rates from 1.57 to 6.28 L/m2.d were used throughout the study period in two treatments. The effect of disc rotation speed on system efficiency was also investigated. The results indicated that COD removal efficiency decreased with increasing hydraulic loading rate but increased with increasing disc speed from 5 to 15 rpm. The best removal efficiencies of 88 and 86 percent for RBCI and RBCII, respectively, were obtained for an aniline concentration of 400 mg/L, a hydraulic loading rate of 1.57 L/m2.d, and a disc speed of 15 rpm. Based on the results, although both systems yield almost equal efficiencies, the start-up period was shorter in RBCII with a clearer effluent due to the lower quantity of suspended microorganisms in the reactor than that in RBCI. Use of packing may decrease energy consumption for disc rotation due to the overall weight reduction of the system.

To investigate the effect of gamma irradiation on the disinfection of water and wastewater, water samples were collected from raw and filtered water and wastewater samples were taken from the effluent of the secondary sedimentation, polished effluent (1-day retention time), and also from filtered (rapid sand filter) effluent. The samples were irradiated with gamma collimated beam in a batch system using a Co-60 therapeutic gamma radiation machine with a radioactive source emission rate of 405.38CGy/min at different doses of 20-160 Gy and 80-240 Gy, respectively. The samples were analyzed before and after irradiation for total and fecal coliforms. It was observed that nearly 100% reduction was achieved in total and fecal coliforms in water samples treated with a dose of 160 Gy. Depending on effluent quality, disinfection efficiencies achieved using 240 Gy gamma irradiation for inactivation of total coliforms in wastewater samples were 83, 64, and 56 percent for filtered, clarified, and secondary effluents, respectively. The same values were nearly 81, 58, and 46 percent, respectively, for inactivation of fecal coliforms. At lower doses of 120-240Gy, the coliform bacteria were successfully inactivated. It was concluded that a linear correlation holds between the dose delivered and the inactivation of microorganisms, so that inactivation increases with increasing irradiation time.

Excessive amounts of fluoride in drinking water pose serious health problems. It is, therefore, essential to remove it from drinking water by appropriate water treatment processes down to standard levels for preventing the health risks associated. The aim of this study was to determine the performance of a kind of hybrid sorbent resin in removing fluoride from water. Batch experiments were performed to investigate adsorption isotherms and the effects of experimental parameters such as contact time (5-120 min) and pH (3-9). Results revealed that fluoride adsorption rate was initially rapid but continued at a slower rate before it reached equilibrium in 120 min. Optimum fluoride removal was observed at a pH range of 3-5.5. The data obtained from the experiments were fitted with Langmiur model.

Surfactants are organic chemicals with wide applications as detergents. Linear alkyl benzene sulfonate (LAS) is an anionic surfactant most commonly used. Discharge of raw or treated wastewater containing this chemical into the environment causes major public health problems. In this study, 64 samples were taken from the effluent of Yazd Wastewater Treatment Plant over a period of one year. The samples were analyzed according to standard methods. The results obtained from the samples taken in different seasons showed that the highest efficiency of anionic surfactant removal was achieved in the summer in the secondary facultative stabilization pond. The least efficiency was observed in the autumn in samples from the anaerobic stabilization pond. It was also found that treated wastewater discharged into surface waters, reused for agricultural irrigation, or discharged into absorbent wells had significant differences with Pvalue<0.5 and contained surfactant contents higher than those recommended by environmental standards in all cases.

This paper surveys the different theories developed on anaerobic sludge granulation. The theories are generally categorized as physical, microbial, and thermodynamic approaches. In the physical approach to the granulation process, granulation is described by such physical conditions of the reactor as up flow velocity of gas and liquid streams, suspended solids in the effluent flow, and excess sludge removal. Microbial theories are based on the properties of specific organisms and on granule properties (granule structure and its microbiology). The thermodynamic approach studies such factors as hydrophobia, electrophoretic mobility, effective energy in granule adhesion process, and effect of proton transferring activities on bacterial membrane surfaces.

Developing optimal operating policies for conjunctive use of surface and groundwater resources when different decision makers and stakeholders with conflicting objectives are involved is usually a challenging task. This problem would be more complex when objectives related to surface and groundwater quality are taken into account. In this paper, a new methodology is developed for real time conjunctive use of surface and groundwater resources. In the proposed methodology, a well-known multi-objective genetic algorithm, namely Nondominated Sorting Genetic Algorithm II (NSGA-II) is employed to develop a Pareto front among the objectives. The Young conflict resolution theory is also used for resolving the conflict of interests among decision makers. To develop the real time conjunctive use operating rules, the Probabilistic Support Vector Machines (PSVMs), which are capable of providing probability distribution functions of decision variables, are utilized. The proposed methodology is applied to Tehran Aquifer in Tehran metropolitan area, Iran. Stakeholders in the study area have some conflicting interests including supplying water with acceptable quality, reducing pumping costs, improving groundwater quality and controlling the groundwater table fluctuations. In the proposed methodology, MODFLOW and MT3D groundwater quantity and quality simulation models are linked with NSGA-II optimization model to develop Pareto fronts among the objectives. The best solutions on the Pareto fronts are then selected using the Young conflict resolution theory. The selected solution (optimal monthly operating policies) is used to train and verify a PSVM. The results show the significance of applying an integrated conflict resolution approach and the capability of support vector machines for the real time conjunctive use of surface and groundwater resources in the study area. It is also shown that the validation accuracy of the proposed operating rules is higher that 80% and based on these rules, the cumulative groundwater table variation is limited to 80 centimeters during a 15-year planning horizon.

In recent years, drought and demand growth in most parts of the county have caused a dramatic increase in using groundwater for water supply purposes. Besides, unplanned excessive discharges from aquifers have led to aquifer degradation. In most integrated water resources management models, groundwater simulation is used for taking into account discharge constrains, however, the most cases the optimal solution is not achievable. Hence, artificial neural network models may be replaced by groundwater numerical simulation models. In this paper, a methodology based on dynamic artificial neural networks (DANN) is developed for simulating groundwater table. Karaj aquifer is used as the case study and its groundwater numerical model (PMWIN) is calibrated using such measured groundwater characteristics as hydraulic conductivity and specific storage. The results of the numerical model are presented to DANN for training. In the proposed procedure, the total recharge, discharge, and groundwater level in previous time intervals are used as the inputs to the DANN model. The model output is the groundwater level at the end of the time interval. In this study, the development of the model is accomplished in four steps that consist of developing the aquifer simulation model and its calibration, producing the input/output data set for DANN training, training the DANN model for various structures, and selecting the best structure for use in the optimization model. The result shows that the proposed DANN model is more efficient in simulating groundwater level fluctuations than the static artificial neural network (SANN) models.

In this paper, the continuous Ant Colony Optimization Algorithm (ACOR) is used to investigate the optimum operation of complex multi-reservoir systems. The results are compared with those of the well-known Genetic Algorithm (GA). For this purpose, GA and ACOR are used to solve the long-term operation of a three-reservoir system in Karkheh Basin, southwestern Iran. The solution must determine monthly releases from the three reservoirs and their optimum allocations among the four agricultural demand areas. Meanwhile, a minimum discharge must be maintained within the river reaches for environmental concerns. Review of past research shows that only a few applications of Ant Colony have been generally made in water resources system problems; however, up to the time of initiating this paper, we found no other application of the ACOR in this area. Therefore, unlike GA, application of Ant-Colony-based algorithms in water resources systems has not been thoroughly evaluated and deserves serious study. In this paper, the ACOR is studied as the most recent Ant- Colony-based algorithm and its application in a multi-reservoir system is evaluated. The results indicate that with when the number of decision variables increases, a longer computational time is required and the optimum solutions found are inferior. Therefore, the ACOR would be unable to solve complex water resources problems unless some modifications are considered. To overcome a part of these drawbacks, a number of techniques are introduced in this paper that considerably improve the quality of the method by decreasing the required computation time and by enhancing optimum solutions found.

Identification of the net groundwater recharge is essential for groundwater modeling and water resources management. Due to its simplicity and its minimum stochastic parameter requirements, the CRD model has been widely applied to estimate recharge. Rainfall is the main source of groundwater recharge in the Karvan area in Isfahan (Iran). The area is located in the semi-arid zone and there is no source of recharge other than rainfall. Estimation of groundwater recharge from rainfall is not an easy task since it depends on many uncertain parameters. The cumulative rainfall departure (CRD) model was used in this study to estimate the net groundwater recharge from rainfall. The cumulative rainfall departure (CRD) model is based on the principle of water balance and does not require much data as is the case with other conventional methods of recharge estimation. The CRD model was conducted using the optimization method to minimize the root mean square error (RMSE) between the measured and the simulated groundwater head. The results indicated that the annual amount of groundwater recharge from rainfall in the Karvan Aquifer in Isfahan is about 48.07 million m3.

Reusing organic wastes such as sewage sludge is a good way of returning organic matter and nutrients into soil. However, the likelihood of heavy metal accumulation in soil and the subsequent contamination of human and animal food chains should be determined before any attempt is made to reuse organic wastes. The purpose of this greenhouse study was to investigate the impact of sewage sludge from the effluent of Isfahan Polyacryl Corp. on yield and shoot cadmium concentrations in corn as compared to the animal manure and mineral salt of cadmium. A completely randomized design with three treatments (cow manure, sewage sludge at 25 Mg/ha, and 5 and 10 mg/kg Cd as CdCl2) was used. Corn seedlings were harvested 60 days after planting and Cd concentration was measured in the shoot. Application of animal manure and sewage sludge significantly increased corn biomass. Cadmium increment, in both organic and mineral salt treatments, significantly increased cadmium concentration in the corn shoot. The plant cadmium concentration in the soil treated with industrial sewage sludge and CdCl2 were significantly higher than that treated with animal manure.

The main objective of the present work is to predict the longitudinal dispersion coefficient in natural streams using a neural network (NN) model which was developed based on Quasi-Newton training functions. For this reason, we used the hydraulic and geometric data easily obtained in natural streams. A total number of 100 data sets was used which were split into three subsets: training, validation, and testing sets. The most cited literature in the field was first reviewed in an attempt to identify possible deficiencies and inadequacies in previous studies. In a second stage, a new approach less commonly used by researchers, i.e. the NN model based on Quasi- Newton training functions was employed for predicting the longitudinal dispersion coefficient in natural streams. Finally, the effect of Quasi-Newton training function on the performance of the NN model was investigated and the best architecture was selected for the model developed. The results obtained in this study showed that the proposed model enjoys a satisfactory level of accuracy. The two statistics of the model, i.e. determination coefficient and mean absolute error in testing step, were found to be equal to 0.85 and 53, respectively.