WATER AND WASTEWATER
Year:2018 | Volume:29 | Issue:1 |Papers Count:11

Price elasticity plays a critical role in determining water tariff and its system. Many economic decision makers and researchers have estimated demand function for different cities in order to predict the associated income and price elasticity. In this research we reviewed 20 studies on urban domestic water demand function from which 63 price elasticity values were obtained. Since the price elasticity values obtained from these studies had significant statistical differences, the aim of this research is to determine the effective factors in price elasticity values as well as to analyze differences in such values using meta-analysis technique. The meta-analysis technique focuses on variation in water price elasticity results. The statistical meta-analysis technique focuses on two main objectives of publication bias or publication heterogeneity in reported results. The results indicated that publication bias is negligible while publication heterogeneity is significant. The major factors affecting price elasticity values are classified into 4 categories including theoretical, model, data and socio-geographical specifications. The result indicated that variables such as income, time-series datasets, natural logarithm function and use of stone-geary theory which is the basis for predicting many domestic water demand functions, significantly overestimate the price elasticity values. Also the geographical condition of the region, population density and use of OLS technique to estimate the demand parameters underestimates the price elasticity values.

Nowadays water loss has been turned into a global concern and on the other hand the demand for water is increasing. This problem has made the demand management and consumption pattern reform necessary. One of the most important methods for managing water consumption is to decrease the water loss. In this study by using neural networks, a new method is presented to specify the location and quantity of leakages in water distribution networks. In this method, by producing the training data and applying it to neural network, the network is able to determine approximate location and quantity of nodal leakage with receiving the nodal pressure. Production of training data is carried out by applying assumed leakage to specific nodes in the network and calculating the new nodal pressures. The results show that by minimum use of hydraulic data taken from pressures, not only this method can determine the location of nodal leakages, but also it can specify the amount of leakage on each node with reasonable accuracy.

One of the most important operational issues in urban drinking water production and distribution systems is to assign a plan for running hours of water supplying electric pumps. The cost of consuming electricity in these pumps allocates most of water and wastewater companies operational costs to itself which is dependent to their running hours. In this paper, meanwhile having a field study in Sistan rural water and wastewater company, the constraints for specifying electric pumps operational time in water supplying resources such as restrictions in fulfilling demand, supply potable water with suitable quality and uselessness of electric pumps have been identified. Due to uncertainty and fuzziness of the constraints, a linear programming model with fuzzy restrictions for determining electric pumps running hours per day is submitted with the aim to minimize electricity consumption and cost. After collecting and using required data for model, it proved that using the proposed model could reduce the costs of electrical energy and increase productivity up to 23 percent per month. The proposed mathematical fuzzy programming is able to specify electric pumps scheduling plan for water supply resources with the aim to reduce the costs of consuming energy.

The ram pump is a device which pumps a portion of input discharge to the pumping system in a significant height by using renewable energy of water hammer. The complexities of flow hydraulic on one hand and on the other hand the use of simplifying assumptions in ram pumps have caused errors in submitted analytical models for analyzing running cycle of these pumps. In this study it has been tried to modify the governing analytical model on hydraulic performance of these pumps in pumping stage. In this study by creating a logical division, the cycle of the ram pump was divided into three stages of acceleration, pumping and recoil and the governing equations on each stage of cycling are presented by using method of characteristics. Since the closing of impulse valve is nonlinear, velocity loss in pumping stage is considered nonlinearly. Also the governing equations in pumping stage were modified by considering disc elasticity of impulse valve and changing volume of the pump body when the water hammer phenomenon is occurred. In order to evaluate results and determine empirical factors of the proposed analytical model, a physical model of the ram pump is made with internal diameter of 51 mm. Results of this study are divided into several parts. In the first part, loss coefficients of the impulse valve were measured experimentally and empirical equations of drag coefficient and friction coefficient of the impulse valve were submitted by using nonlinear regression. In the second part, results were evaluated by using experimental data taken from this study. Evaluation of statistical error functions showed that the proposed model has good accuracy for predicting experimental observations. In the third part, in order to validate the results in pumping stage, the analytical models of Lansford and Dugan (1941) and Tacke (1988) were used and the error functions resulted from prediction of experimental observations were investigated through analytical models of the previous researchers. Comparing the results indicates that in the proposed model, noticing that the recommended equations of pumping stage are presented based on nonlinear closing theory of the impulse valve, the model accuracy for predicting relative pumping rate has been increased up to 3% compared with linear closure theory (Lansford and Dugan, 1941) and has been increased up to 5% compared with rapid closure theory (Tacke, 1988).

Chlorination unit is widely used to supply safe drinking water and removal of pathogens from water distribution networks. Data-driven approach is one appropriate method for analyzing performance of chlorine in water supply network. In this study, multi-layer perceptron neural network (MLP) with three training algorithms (gradient descent, conjugate gradient and BFGS) and support vector machine (SVM) with RBF kernel function were used to predict the concentration of residual chlorine in water supply networks of Ahmadabad Dafeh and Ahruiyeh villages in Kerman Province. Daily data including discharge (flow), chlorine consumption and residual chlorine were employed from the beginning of 1391 Hijri until the end of 1393 Hijri (for 3 years). To assess the performance of studied models, the criteria such as Nash-Sutcliffe efficiency (NS), root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (CORR) were used that in best modeling situation were 0. 9484, 0. 0255, 1. 081, and 0. 974 respectively which resulted from BFGS algorithm. The criteria indicated that MLP model with BFGS and conjugate gradient algorithms were better than all other models in 90 and 10 percent of cases respectively; while the MLP model based on gradient descent algorithm and the SVM model were better in none of the cases. According to the results of this study, proper management of chlorine concentration can be implemented by predicted values of residual chlorine in water supply network. Thus, decreased performance of perceptron network and support vector machine in water supply network of Ahruiyeh in comparison to Ahmadabad Dafeh can be inferred from improper management of chlorination.

Ceftriaxone is widely used for healing the infectious diseases. Not being treated in hospital wastewater treatment systems, this antibiotic has the possibility of entering into the aqueous environmental and the danger of microbial resistance in these environments also increases. This study aims to examine the efficiency of catalytic ozonation process by using magnesium oxide nanoparticles to remove ceftriaxone from aqueous solutions. In this study, the effect of pH, ozone dose, reaction time, catalyst dose and initial antibiotic concentration for ceftriaxone removal were studied using a cylindrical-shaped experimental reactor through semi-batch method. The residual concentration of ceftriaxone was determined by spectrophotometer. The experiment was carried out based on the one factor at a time method and finally the optimum efficiency of ceftriaxone removal and its desired conditions were determined. The best condition of the reactor obtained in pH 11, the catalyst dose of 1g/l, ozone dose of 18. 384mg/min, pollution concentration of 10mg/l and reaction time of 30 min which showed 86% of ceftriaxone removal. Due to an increase in ozone retention time, the use of magnesium oxide nanoparticles in ozonation process also increased the antibiotic removal. It leads to a decrease in treatment costs because of a decrease in ozone consumption in an industrial scale.

The effluent from copper industries is important in terms of production volume, environmental pollution and cost of treatment and disposal. In addition to its economic reasons, the effluent recovery also reduces the pollutant load. Such cases therefore prove the necessity of investigating different methods for reducing water consumption and effluent generation. This study investigated the minimization of water consumption and effluent production in Khatoon Abad copper refinery plant. For this purpose, the conventional method of reducing water consumption and a new technology called water pinch were explained. The water pinch technology was examined for three pollutant indices (TSS, TDS and turbidity) in three high consumption units of Khatoon Abad copper refinery plant. For TSS, TDS and turbidity as pollutant indices, total 614, 525 and 451 cubic meters of raw water consumption per day equivalent to 40. 96, 35 and 30 percent could be saved, respectively. According to the results, the pinch technology could help researchers to make decisions for reducing water consumption and according to the observations, suspended solids are regarded as the basis for reducing water consumption. Minimization of water consumption, Water pinch, Mass transfer networks, Water reuse, Total suspended solids, Turbidity.

Industrial and municipal wastewater often contains metal ions that once they are in excess of permitted limit they can be harmful to humans and aquatic animals’ health. Zinc is one of the pollutants which damages the aquatic environment and endangers the health of humans, animals and plants. In this study, graphene oxide (GO) with high surface area along with natural halloysite nanotubes (HNTs) and iron oxide nanoparticles are used as M-HNT /GO magnetic nanocomposite for the removal of zinc ions from aqueous solutions. In this study, magnetic halloysite nanotube/ graphene oxide nanocomposite (M-HNT/GO) was synthesized and then used as a nano-adsorbent for zinc removal from aqueous solutions. The M-HNT/GO nanocomposite was investigated and identified by using FT-IR, XRD and SEM methods. Vibrating sample magnetometer technique (VSM) was used to measure magnetic properties. The effect of different parameters such as pH, initial concentration of metal, temperature, stirring speed, adsorbent dosage and contact time on zinc removal by M-HNT/GO were investigated and optimum values were determined. Then, efficiency of fabricated nano-adsorbent was studied for removal of zinc ions from real sample. Optimal conditions for zinc ions removal were found at pH 6, the contact time of 90 minutes, the ion concentration of 2ppm, the GO to HNT and Fe3O4 ratio of 1: 1: 10 respectively, the buckling speed 250 rpm, the amount of adsorbent 005 g and the temperature of 25 ° C. The study of adsorption isotherms indicated the best fitting of experimental data with Fredlichl isotherm model. To describe kinetic data, pseudo first order and pseudo second order kinetic models were used. Dynamic data for zinc metal was adapted to pseudo second order kinetics model. The results showed that synthesized nano-adsorbents could be used with 95. 2% ± 0. 41% for zinc removal from wastewater contaminants. Furthermore, the M-HNT/GO nano-adsorbents can be recycled and reused frequently.

In this study, composite hydrogel was provided based on carboxymethyl cellulose and nano-sized montmorillonite by situ radical polymerization method and evaluated as copper cation adsorbent from aqueous solution. Synthesis of hydrogel composite and the effect of various parameters such as pH of solution, adsorbent dosage, initial concentration of copper cation, presence of lead and cobalt cations in solution and contact time on adsorption capacity of copper were studied through batch experiments. The results of FTIR spectra showed that functional groups like Si-O-Mg, Si-O-Al and – COOH exist in synthesized hydrogel. The results indicated that with pH increasing of initial Cu2+ solution up to 5, the adsorption capacity increased subsequently. The experimental kinetic and equilibrium data had the most conformity with pseudo-second order kinetic model and Langmuir adsorption isotherm. The maximum obtained adsorption capacity were 111, 100 and 83. 3 mg/g at 273, 303 and 333K respectively. The results showed that the Cu2+ ion adsorption is favorable, also the standard free Gibbs energy and standard enthalpy indicated a spontaneous and exothermic adsorption process. In competitive adsorption experiments, composite hydrogel tended to adsorb cations with an affinity order of Pb2+>> Cu2+ > Co2+.

Recently the low-cost adsorbent for heavy metals removal such as Lead in recent years has been noticed by researchers. In this study, lead removal by clinoptilolite has been investigated. Non-continuous lead absorption from aqueous solution was carried out by using clinoptilolite. The zeolite characteristics were analyzed with SEM and XRD. The effect of pH variables (1, 3, 5, 7, 10), contact time (15, 30, 45, 60, 90 min), clinoptilolite adsorbent dosage (1, 3, 5, 7, 10 g) and lead concentrations (10, 20, 40, 50, 60, 70, 80 & 100 mg/L) were studied on lead removal efficiency and adsorption isotherms and kinetics in constant temperature and stirrer speed of 250 rpm. According to the results, optimum conditions of lead removal by natural zeolite were pH of 8, adsorbent dosage of 5 g/L and contact time of 45 min., the highest removal efficiency of 89. 6 achieved. By increasing metal concentration, the amount of removed lead was decreased while absorption capacity increased. Of the two studied models, the Langmuir isotherm had better conformity for lead adsorption (R2=0. 99) than other isotherms. Also the pseudo second order kinetics model had better conformity with achieved data than other models. Results of this study indicated that natural clinoptilolite zeolite can be considered as an efficient and cost effective adsorbent for lead removal from aqueous solutions.