WATER AND WASTEWATER
Year:2011 | Volume:22 | Issue:1 (77)|Papers Count:19

Water supply management, both in terms of quality and quantity, is facing serious problems due to growing municipal, industrial, and agricultural demands. Disinfection and bacterial removal from water by chemical and/or physical treatment processes are the minimum requirements in any water distribution system. Disinfection can be performed in a variety of ways, the most common and cheapest being chlorination. Selecting proper injection points in the network and determining chlorine dosages are basic considerations in maintaining chlorine residual at standard levels at nodes across the network and minimizing operation costs. Minimum chlorine residual levels must be determined so as to prevent bacterial growth in water and maximum levels should not be exceeded in order to avoid customer complaints about taste and smell or to inhibit the formation of potentially toxic by-products. Improper water quality management with respect to chlorine residual levels has at times led to serious problems to occur in many parts of the world. In this paper, the EPANET software package capable of water quality and hydraulic simulations has been integrated with a Genetic Algorithm nonlinear optimization model to derive a combined model for optimizing chlorine dosage. Two real-life examples adapted from water distribution networks have been used to verify the efficiency of the proposed model in determining optimal chlorine dosage. The results indicate that chlorine residual at nodes in water supply networks can be maintained at standard levels if chlorine injection is accomplished in more than one reservoir and if these reservoirs as injection points are properly selected. Application of the model led to a decrease in the total chlorine consumption and to an increase in the number of nodes where chlorine residual met the standard.

The efficiency of particle settling in tube settlers can be improved by reducing tube diameter; however, this has limits due to the clogging at low diameters. In this study, effect of reduced tube diameters on enhanced efficiency of tube settlers was investigated. This improvement was achieved by staging of the operation and reducing tube diameter in the last stage of the multi-stage tube settlers. Theoretical analysis indicated that the efficiency of tube settlers would improve if tube settlers were used in different stages in series and that if their diameter was reduced in the last stage. The predictions from the theoretical analysis were tested in a pilot study.One- and two-stage tube settler pilot plants were used to remove water turbidity caused by clay in the first and second phases of tests with and without adding coagulant, respectively. The tube diameter in the one-stage tube settler were 5.0 cm, and those in those the first and second stages of the two-stage tube settler was 5.0 and 1.2 cm, respectively. The results of both phases showed that the two-stage tube settler was more efficient than the one-stage for turbidity removal with no clogging observed because the majority of the particles had been removed in the first stage.

For the purposes of this study, activated carbon was derived from pine-cone by a chemical-thermal process.Initially, its chemical and physical properties were determined before it was used for the removal of Acid Black 1 and Acid Blue 113 dyes. A batch sorption study was carried out in order to obtain the optimum isotherm model. The monolayer maximum saturation capacities of AB1 and AB113 dyes based on Langmuir isotherm model were determined to be 458 mg dye/g carbon and 286 mg dye/g carbon, respectively. Adsorption of dyes was also studied in a continuous-flow state using a fixed-bed column of activated carbon. The effects of operating variables such as flow rate, bed depth, and dye concentration on the column operation were studied.Data confirmed that the breakthrough curves depended on flow rate, bed depth, and initial dye concentration.Column behavior was investigated using Thomas Model and model parameters were determined by a non-linear regression method. The Langmuir and Freundlich isotherm models were used to fit the experimental data. The best fit of the adsorption isotherm data was obtained using the Langmuir model for both dyes. The results showed that Thomas Model was suitable for the description of breakthrough curves under the experimental condition. The column adsorption capacity was also compared with equilibrium adsorption capacities for each dye.

The aim of this study to synthesize nanoparticle zero valent iron and to determine its efficiency in arsenic removal from aqueous solutions Nanoparticles were synthesized by reduction of ferric chloride using sodium borohydrid. The experiments were conducted in a batch system and the effects of pH, contact time, and the concentrations of arsenit, arsenat, and nano zero valent iron were investigated. SEM and XRD were applied for the determination of particle size and characterization of the nanoparticles synthesized. SEM results revealed that synthesized particles were of nano size (1-100 nanometers). At pH=7.0, 99% of arsenit and arsenat was removed when nano zero valent iron concentration was 1 (g L-1) over a retention time of 10 min. Based on the results obtained, the removal efficiency was enhanced with increasing nano zero valent iron dosage and reaction time, but decreased with increasing initial concentration and initial solution pH. The significant removal efficiency, high rate of process and short reaction time showed that iron nano particles are of a significant potential for the removal of arsenic from aqueous solutions.

Surface and groundwater resources are increasingly jeopardized by discharges from pharmaceutical, chemical, and detergent plants. The high pollutant load of the effluents from these industries requires specific treatments.The objective of this research was to study and compare the nanofiltration and adsorption hybrid system with the plain nanofiltration system in wastewater treatment. For this purpose, a pilot nanofiltration system with a capacity of 7.6 m3/d using 1 and 5 micron filters and a FILMTEC NF90-4040 membrane was used in the first phase of the study. In the second phase, granular activated carbon cartridges were used. Inluent and effluent discharges as well as the COD removal were measured in both systems under variable times and organic load conditions. The results showed that COD removal efficiency was higher in the hybrid system than in the plain naonofiltration one. In the hybrid system, the Maximum in the hybrid system, the COD removal efficiencies achieved for organic loads of 1000, 2000, and 3000 mg/L were 99%, 95.86%, and 92.93%, respectively. The same values for the plain nanofiltration system were 87.34%, 50%, and 29.41%, respectively. It was found that polarization and membrane fouling decreased both the effluent flow and the COD removal efficiency with time. Fouling of the membrane was, however, lower in the hybrid system compared to the plain nanofiltration; thus, the hybrid system was associated with higher values of COD removal and delayed membrane fouling.

Linear Alkyl Benzene Sulphonate Acids (LAS) are one of the anionic surfactants that are produced and used in large quantities in different countries and find their way into the natural environment through sewer systems.These compounds may potentially cause environmental hazards in such surface waters as rivers. It is, therefore, necessary to remove as much of these compounds as possible by biological processes in wastewater treatment plants. For this purpose, four parallel biological reactors were constructed that used the conventional activated sludge and aeration tanks with fixed bed on the bench scale in order to evaluate the removal efficiency of LAS.The reactors were operated under conditions similar to domestic wastewater treatment plants. Parameters of interest were measured according to standard methods and ANOVA and T-test were used for the statistical analysis of the data. The results showed that aeration tanks with fixed beds yielded higher values of LAS and COD removal and air consumption compared to the conventional activated sludge system. It was shown that the two systems studied achieved LAS removal efficiencies of 96% and 94% for an influent LAS concentration of 5 mg/L. Further, it was found that the effluents from both systems satisfied water quality standards for discharge into surface waters (<1.5 mg/L) and into ground waters (<.5 mg/L). At LAS concentrations higher than 5 mg/Li, the conventional activated sludge system did not have the required efficiency and its effluent did not meet the environmental discharge standards. In contrast, aeration tanks with fixed bed achieved a removal efficiency of 97% at higher concentrations (15 and 20 mg/L) and were capable of reducing the amount of effluent LAS concentration. Therefore the aeration tank system should be recommended for high LAS concentrations in the influent.

Mathematical modeling allows a large number of potential aerobic digestion process designs to be evaluated and the performance of aerobic digesters to be predicted. In applying such models, however, it is essential to use appropriate models that can neatly explain the kinetics of the process. In this research, the simple first-order model (known as Adams model) and Activated Sludge models developed by International Water Association (ASM1 and ASM3) were evaluated. For this purpose, three batch reactors with initial TSS concentrations of 5, 000, 10, 000, and 20, 000 mg/L and operating volumes of 10 L were operated to collect the experimental data required for model calibrations. Batch reactors were run and monitored for 70 days. Samples taken from the reactors were analyzed for their solids concentrations and their Chemical Oxygen Demand (COD). The measured data were then used to estimate the parameters of the models investigated and the best sets of parameters yielding the best fit of the predicted and measured data were identified for each model and for each reactor. The parameter estimation results for batch runs showed that higher sludge concentrations were associated with lower cell decay coefficient values. The error values for batch runs calculated by ASM3 were found to be lower than those calculated by Adams model and by ASM1. Comparisons with experimental data proved ASM3 to be superior in terms of its capability to predict concentrations in aerobic digestion batch reactors.

This paper investigates the effects of factors involved in sludge anaerobic granulation. Granulated sludge formation is the main parameter contributing to the success of UASB reactors. Anaerobic granulation leads to reduced reactor size, space requirement, and investment costs. Operation costs are also greatly reduced due to lack of aeration. An important parameter affecting process performance is the size of sludge granules; the factors involved in granule size will be investigated. Some of the important parameters of anaerobic sludge granulation are: existence of growth cores as inert particles or granulated sludge, process operational conditions (Sludge Loading Rate and Organic Loading Rate, Loading rate increase and …), and environment conditions (nutrients, temperature, pH, combination and …).

In this study, a neural network model is proposed for modeling leachate flow-rate in a municipal solid waste landfill site. After training, the neural network model predicts leachate generation based on meteorological data and leachate characteristics. Parameters such as pH, temperature, conductivity and meteorological data were used as input data. To validate the proposed method, a case study was carried out based on the data obtained from city of Beirut landfill site. While waste disposal at the site started in October 1997, measuring leachate generation rates was not initiated until April 1998. The Levenberg-Marquardt algorithm was selected as the best of thirteen backpropagation algorithms. The optimal neuron number for Levenberg-Marquardt algorithm is 10.The performance of modeling was determined. According to the statistical performance indices (R=0.976, ARE=0.089), the results of the forecast model were in good agreement with measured data.

In this paper, a three-zone mathematical model is developed for soil pollutant removal by phytoremediation which is based on the root growth and decay in the system. The variables of the model are based on parts which have different volumes and a given first order decay coefficient. When the root moves in the soil, soil lies in the cycles of layers near the root (rhizosphere), root decay zone, and soil zone. It follows then that despite the fact that the model is based on the assumption of immobile pollutant, the fact that root moves in the soil ensures that soil meets the rhizosphere. To account for the root growth, a model is developed that involves both its spatial growth (exponential variation with depth) and its temporal growth (sinusoidal variation with time).

This paper studies the mechanical characteristics of saltation of bed-load particles in turbulent flows.Experiments have been carried out by means of high speed photography to obtain saltation characteristics such as length, height, velocity, impacting and rebounding angles for different hydraulic conditions, particle size, density and bed roughness by analyzing and processing pictures taken at 250 frames per second. The measured values of the parameters showed that increases in turbulence parameters of the flow increase saltation length, height, and velocity but decrease the impacting and rebounding angles. It was also found that saltation length and height tended to increase as the average size of the bed material (d50) increased. To solve numerical trajectory equations, the initial velocity component of the particle must be known. The initial longitudinal particle velocity was found to range from 3u* to 8u* and its vertical velocity ranged from 1.5u* to 3.5u*, which were different from the values reported in the literature. The longitudinal and vertical components of the restitution coefficient were also measured and it was seen that the Shields parameter (t*) had no meaningful effect on these components, remaining almost constant with increasing.

Weirs are among the most common hydraulic structures that have been used for centuries by hydraulic engineers for flow measurement, energy dissipation, flow diversion, regulation of flow depth, and flood passage. Side weirs, or lateral weirs, are essentially free overflow weirs installed along the side of the main channel to divert flow over them when the surface of flow in the channel rises above their crest. These weirs are often used in irrigation and flood regulation systems, urban drainage, and many other water resources and environmental projects. The flow over side weirs falls under the category of spatially varied flow. In this paper, methods are presented based on analytical and experimental models for improving side weir performance. For this purpose, groups of (one, two, and three) vane plates or piles were employed. Analytical models were developed based on momentum and continuity equations for determination of dynamic force on vane plates or piles, water surface profile and discharge coefficient of side weir. Measured data were used for calibrating the analytical models and for presenting expressions for the discharge coefficient. Results show that the diverted discharge coefficient can be increased by up to 30% compared to the simple side weir discharge coefficient.

This present study was conducted to determine particle size characteristics of suspended sediments transported by base and flood-flows in the Kojur River in Kojur forest watershed with an area of 50000 ha. Sediment samples were analyzed based on Stokes’ law using a modified pipette and applying the GRADISTAT software.Results showed 81.1, 3.4 and 15.3% for sand, silt, and clay, respectively, in base flow conditions while under flood conditions, the same values were 56.5, 17.0 and 26.5%.

The main goal of this research is to evaluate the role of input selection by Principal Component Analysis (PCA) on Support Vector Machine (SVM) performance for monthly stream flow prediction. For this purpose, SVM is used to predict monthly flow as a function of 18 input variables. PCA is subsequently employed to reduce the number of input variables from 18 to 5 PCs which are finally fed into the SVM model. SVM and PCA-SVM models are evaluated in terms of their performance using a developed statistic by the authors. Findings show that preprocessing of input variables by PCA improved SVM performance.