Journal of Applied Research in Water and Wastewater
Volume:6 Issue: 2, Summer-Autumn 2019

In this paper, for the first time, the discharge coefficient of triangular plan form weirs is simulated by the extreme learning machine (ELM). ELM is one of the powerful and rapid artificial intelligence methods in modeling complex and non-linear phenomena. Compared to other learning algorithms such as back propagation, this model acts rapidly in the learning process and provides a desirable performance in processing generalized functions. In this study, the Monte Carlo simulation is used for examining capabilities of numerical models. Also, the k-fold cross validation method with k=5 is utilized for evaluating abilities of the ELM models. Then, six ELM models are introduced by means of the parameters affecting the discharge coefficient of triangular plan form weirs. After that, the superior model is identified by analyzing the results of the mentioned models. The superior model predicts discharge coefficient values with reasonable accuracy. This model simulates the discharge coefficient as a function of the flow Froude number, vertex angle of the triangular plan form weir, the ratio of weir length to its height, the ratio of flow head to weir height and the ratio of channel width to weir length. For the best model, the Mean Absolute Error, Root Mean Square Error and determination coefficient are computed 1.173, 0.012 and 0.967, respectively. Furthermore, examination of the influence of the input parameters indicates that the flow Froude number is the most influenced factor in modeling the discharge coefficient. Also, the error distribution showed that roughly 86 % of the superior model results had an error less than 2 %. Furthermore, a practical equation was provided to compute the discharge coefficient.

The frequent occurrences of wet and dry in the catchment area of the Gamasiab river located in the west of Iran, in addition to affecting the quantitative status of surface water, has caused changes in the water quality of the basin. Therefore, modeling and prediction of Gamasiab river water quality in wet and dry periods are research priority. In this study, an optimized artificial neural network (ANN) trained with three different optimization algorithms namely; particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA) was proposed for predicting the electric conductivity (EC). For this purpose, water quality data from 1967 to 2017 collected at the hydrometric station in the Gamasiab river were used for developing and testing the models. First, the study program was divided into two periods of wet and dry, this classification based on flow rate in the river. Then, in a preliminary statistical analysis, the effective parameters were determined for EC estimation. The performance of the applied methods showed that the ANN optimized using ICA algorithm was better than the ANN optimized with GA and PSO, and also the standard ANN without optimization. Overall, the ANN optimized with ICA has higher R and lower MARE and RMSE, with values of 11.56, 19.63 and 0.93, during the dry period, and 10.63, 17.19 and 0.97 during the wet period, respectively.

The objective of distribution and delivery of water canal scheduling in irrigation canal networks is timely and adequate delivery of water with minimum operational stages of the head gate of supply canal in the presence of canal capacity and irrigation rotation period constraints. In this paper, two objective functions, namely, the number of gate changes and the mean discharge for two networks, were minimized by the Genetic and NSGA-II algorithms. The results showed that minimizing these two objective functions at the same time leads to fewer gate changes compared to the only mean canal discharge objective function in both algorithms. It means the mean discharge objective function cannot minimize the number of operational stages alone. Also the optimization by NSGA-II algorithm did not make a significant difference in the results in comparison with the genetic algorithm for both objective functions. However, in NSGA-II algorithm, it is not necessary to determine the weight of each of the objective functions.

In this paper, the discharge coefficient of weirs is simulated by the extreme learning machine (ELM). To this end, seven different ELM models are introduced by the input parameters. Also, the most optimal number of the neurons in the hidden layer is computed 7. Furthermore, different activation functions of the ELM model are assessed and the sigmoid activation function is taken into account as the most optimal one. Besides, the seven defined ELM models are analyzed and the superior model is introduced. This model approximates the discharge capacity with better performance in comparison with the other ELM models. It should also be noted that the superior ELM model is in terms of the dimensionless factors including Fr, HT/P, Lc/W, A/w, w/P. For the superior ELM model, the R2, VAF and NSC are respectively estimated 0.897, 89.626 and 0.892. Furthermore, the MAE and RMSE statistical indices for the ELM model are respectively estimated 0.024 and 0.031. Also, the most effective input parameters for modeling the discharge capacity of labyrinth weirs using the ELM are detected through the conduction of a sensitivity analysis, meaning that the HT/P is identified as the most influenced input parameter. Lastly, an applicable equation for computing the discharge capacity of labyrinth weirs is suggested which can be used by hydraulic and environmental engineers.

It is important to design and optimize the dimensions of the dam drainage system to keep the dam's downstream shell dry and to prevent the increase of pore water pressure in the earth dam body. It will also be possible to find the minimum factor of safety (FOS) to reduce construction costs by optimizing the drainage dimensions. In this study, Marvak earth dam was modeled by GeoStudio software with real material parameters, and by changing the dimensions of drainage, the material of the material, and slope of the dam, the minimum factor of safety of the dam was obtained. To predict the minimum factor of safety, the software results were used in different cases in the two-layer neural network. By training the neural network from the data obtained from the modeling of the Marvak dam, the minimum factor of safety for horizontal drainage was obtained. To optimize, a command appropriate to the neural network function is taught, by which the optimal values of the dam parameters are calculated. The results of the study show that the two factors of the internal friction angle of the drainage material and the slope of the dam have the greatest impact on determining the minimum factor of safety of the dam.

One of the most important issues in planning and managing water resources is the accurate estimation of monthly input discharge of the reservoirs in the future years, which is always associated with uncertainty. To cover these uncertainties, synthetic stream flow data generation models have been used by various researchers to generate stochastic time series. The computational basis of different stochastic models for generating monthly data has been different and this can have a significant effect on their performance. Therefore, selection of the best model of stochastic data generation for accurate planning and management of a water resource system is one of the major concerns of water resources specialists. In this research, the performance of parametric models of synthetic stream flow generation including Thomas-Fiering, Fragment and ARMA (1,1) and ARMA (1,2) combined with Valencia-Schaake and Mejia and Rousselle models were compared and evaluated. For this purpose, 30 years data of monthly discharge of Marun river in Khuzestan province were used and 900 synthetic monthly flow time series were generated using each of the models mentioned above. Based on the obtained results, the ARMA (1,2) model combined with the Valencia-Schaake model was recognized as the best model, considering the very desired performance in preserving the statistical parameters of historical data and generating maximum and minimum discharges related to wet and dry periods in different probabilities. This model can be used with greater confidence to analyze river systems and reservoirs, manage drought and apply water rationing rules in future drought conditions.

A simple spectrophotometric method is developed for the determination of traces of cyanide ion in water samples. Toxic cyanide was reacted with sulfur (S8) dissolved in acetone and converted to non-toxic thiocyanate which is more stable than cyanide also. This product was reacted to Fe (III) to form red ferric-thiocyanate complex. The complex absorption rate in the first two minutes of its formation is related to the cyanide concentration. By measuring the complex absorbance at 465 nm, the cyanide in the range of 2.0-16.0 μg/mL was determined with a detection limit of 1.7 μg/mL. Relative standard deviation (n = 6) for concentrations of 4.0 and 12.0 μg/mL of cyanide was obtained 4.2 % and 1.5 %, respectively. To avoid interferences from other cations and anions, it is possible to isolate cyanide with a system converting CN- to HCN. This method was successfully applied to determine cyanide in various water samples.

The main purpose of this study was to investigate the effect of different operational parameters on performance and fouling trends of unfilled- polyethersulfone, 0.5 wt.% boehmite-polyethersulfone, tannic acid coated boehmite-polyethersulfone nanofiltration membranes during filtration of Licorice aqueous solution as model foulant. The impact of hydrodynamic conditions (such as transmembrane pressure and cross-flow velocity) and feed composition on permeation, fouling trends and rejection capability were evaluated using lab-scale cross-flow filtration set-up. The applied transmembrane pressure and cross-flow velocity were various in range of 6-12 bar and 0.5-2.5 cm/s, respectively. The results indicated that although, increasing of operational pressure and cross-flow velocity can enhance the permeability and rejection capability of NF membranes also incur appearance of the more severe fouling phenomenon. The least fouling for NF membranes was occurred at the lowest licorice concentration of 0.1 g/l. The rejection percentage of unfilled and embedded nanofiltration polyethersulfone membranes was more than 92 %.

In this research, degradation of Tetracycline by Ag doped ZnO based on Clinoptilolite (Ag-ZnO@CP) as photocatalyst was investigated under visible light. To synthesize of photocatalyst, the wetness impregnation method was used. The synthesized photocatalyst was characterized using XRD, FT-IR, SEM, BET and DRS analysis. The XRD analysis proved the synthesized crystalline phase about 42 nm. Based on SEM images, the morphology of the synthesized particles was spherical with a mean particle size of 55 nm. The FT-IR characteristic peak proved the formation of Ag-ZnO@CP. The photocatalyst bandgap was calculated by the Kubelka-Munk algorithm about 2.95 eV. The bandgap indicated that the photocatalyst was active in the visible light range. The results of degradation were shown that the nanoparticles of Ag-ZnO@CP had a higher efficiency compared with the non-silver state. The efficiency of the synthesized photocatalyst was evaluated with an initial pH of the solution, initial concentration of the pollutant and the amount of photocatalyst. For 60 min irradiation under visible light, the optimal values of the solution pH, the initial concentration of the pollutant and the photocatalyst were 8, 8 ppm and 1 g/L, respectively with 77.2 % degradation of Tetracycline. Also, the photocatalytic degradation of Tetracycline by the synthesized sample follows the first-order kinetic equation.

The effect of ultrasound on cobalt adsorption from aqueous solution onto Fe3O4/Bentonite nanocomposite is investigated. Two layouts of using shaker and sono-separator equipped with ultrasound are considered. The effect of pH on Co(II) ions removal is investigated. Co(II) removal rate increased with increasing pH from 2 to 10, and it reduced after pH=10. For the shaker, the contact time (t) of 50 min is selected as the most effective case. However, for sono-separator the maximum value of Co(II) removal rate is 78% at t=10 min, and it decreased after 10 min. The effect of the adsorbent mass (AM) is investigated and Co(II) removal increased by increasing the specific surface area of the adsorbent. The highest Co(II) removal rates are 83.3% and 86% for the shaker and the sono-separator, respectively. No significant increase for Co(II) removal is observed for increasing AM more than 3 g. The effect of the transducer locations and initial concentration of Co(II) ions (C0) at pH=10 and AM =3 g are investigated. The results showed that the activation of all transducers had the best performance. Initially, with increasing C0 from 0.05 to 0.1 g/L, Co(II) removal rate increased from 84% to 86%, respectively, but with increasing C0 from 0.1 to 0.15 and 0.2 g/L, cobalt removal has been decreased. Finally, the experimental data are adopted with Langmuir and Freundlich isotherms. The comparison of these models showed that both models are well suited to experimental data and data compatibility with the Langmuir model is greater.

ZnO/Fe2O3/Zeolite nanophotocatalyst was synthesized by sol-gel method, and its performance in degradation of ENR, as one of the most commonly used veterinary antibiotics, is evaluated. The synthesized nanophotocatalyst is characterized by XRD, XRF, FT-IR, FE-SEM, EDX, and BET analyses. According to XRD, FT-IR, and EDX, presence of ZnO and Fe2O3 on the zeolite surface is confirmed. Based on XRF results, the optimal molar value of Fe3+/ZnO in the synthesized nanophotocatalyst is obtained as 0.06. The FE-SEM results confirm the deposition of ZnO/Fe2O3 on the zeolite surface and indicate the approximate size of the photocatalyst particles as 48 nm. According to BET results, the specific surface area and pore volume for the synthesized nanophotocatalyst are obtained as 280.16 m2/g and 0.35 cm3/g, respectively. The simultaneous effects of operational factors, including the concentration of pollutant (150-450 mg/l), initial pH of the solution (5-9), and H2O2 concentration (50-200 mg/L) are examined on the ENR degradation efficiency via RSM. The results demonstrate that ENR concentration, pH, and H2O2 concentration have significant impacts on the ENR degradation efficiency in turn. According to the experimental results under optimal conditions (pH, contaminant concentration, and H2O2 concentration: 9, 500 mg/l, and 90 mg/l, respectively), the ENR degradation efficiency is 97.4%. This study suggests that the synthesized nanophotocatalyst has an acceptable efficiency to degrade a non-biodegradable contaminant.

Nowadays, reuse of wastewater is widespread to prevail over shortage of water and to fertilize agricultural lands. This study was conducted to investigate effects of wastewater on some chemical and physical properties of soil. For this purpose, two farms were selected. These farms are located in the Ghahremanloo region at Urmia plain, West Azerbaijan province located in northwestern of Iran. There is no exact information regarding total amount of wastewater delivered to these lands, but flooding irrigation employing wastewater was applied during growing season. The farms are irrigated with two treatments, including wastewater treatment and freshwater where the surface irrigation method was utilized to plant corn. Experiment design was conducted as completely randomized blocks. Each experiment was repeated four times for both freshwater and wastewater treatments. Results of this study showed that the use of wastewater results in a significant decrease in soil's electrical conductivity (EC), sodium absorption ratio (SAR), and a substantial increase in calcium carbonate equivalent (CCE) and organic materials (OM) of the soil. Besides, the wastewater decreased density of Cu and increased density of Zn, Mn, and Fe significantly, known as heavy metals of the soil. However, the density of these elements in the soil was below detection limit. Bulk density also showed a significant reduction in wastewater usage. Finally, paired t-test and Mann-Whitney nonparametric tests were implemented to validate data.