Journal of Applied Research in Water and Wastewater
Volume:5 Issue: 1, Winter and Spring 2018

There are found numerous methods to measure flow in open channels. The simulation of water flow in channel requires mathematic calibration of the structures in channel so that the water level and the discharge become compatible with demand. Sluice gate is one of the most important structure which can perform in free and submerged flow. In this research, there were experiments on a sluice gate mounted in lab flume of 12.5 m, 0.6 and 0.65 length, width and height, respectively, in the slope of 0.0002. Some equations of measuring the discharge from the sluice gate extracted from Energy equations and Momentum were calibrated using two metaheuristic algorithms of simulated annealing and ant colony. After the sensitivity analysis of algorithm was done, the optimal coefficients of discharge obtained for the Conventional equation of discharge in free and submerged flow was obtained 0.686, and 0.881. Also, in calibration of Energy-Momentum method for submerged flow, the optimal contraction coefficient was 0.533. finally, the methods were assessed and compared for which the statistical indexes show the favorability of results.

Manning roughness coefficient is one of the most important parameters in designing water conveyance structures. Unsuitable selection of this coefficient brings up some mistakes. This research aims to present a method to determine the Manning roughness coefficient based on a combination of optimization algorithm of simulated annealing (SA) with gradually varied flow equations. Therefore, in a lab rectangular flume of 12 m, 60 cm and 65 cm in length, width and height with fixed channel bed slope of 0.0002, nine series of water level profiles were carried out. Then, an objective function based on observed and calculated water level gradient was defined to decide on manning roughness coefficient while it was minimized with simulated annealing optimization method. The values of objective function parameters were discussed by sensitivity analysis and the most optimal objective function was obtained. To measure the accuracy of coefficient obtained, Statistics indices of R2, Root mean square error (RMSE), Mean bias error (MBE), d were used. The results showed that manning roughness coefficient has a great accuracy.

In this research, the possibility of polyhydroxyalkanoates (PHAs) production in a mixed microbial culture fed by industrial soft drink wastewater was assessed. To enrich the microbial culture, an uncoupled carbon and nitrogen feeding strategy were used in sequencing batch bioreactor (SBR). To evaluate the efficiency of the strategy, PHA, substrate, dissolved oxygen, biomass and nitrogen concentration profiles were reported in the 16th cycles of the SBR. From the obtained data, COD and nitrogen removal efficiencies were 89 % and 75.5 %, respectively at the cycle time of 12h. Also, the maximum poly-hydroxybutyric acid (PHB) content and specific PHA production rate (qp) were achieved as 13.8% (mg-PHB/mg-TSS) and 6.4×10-3 (mg COD-PHA/mg COD-X.h), respectively.

Present study explored the adsorptive characteristics of eosin Y and red X dyes from aqueous solution onto treated rice husk (TRH). Batch experiments were carried out to determine the influence of parameters likes initial pH, adsorbentdose, contact time and initial concentration on the removal of eosinY and red X. The adsorption kinetics of the two dyes on to TRH was found to follow pseudo-second-order kinetic model. The equilibrium data is successfullyfitted to the Freundlich and Langmuir adsorption isotherm for eosin Y and red X, respectively. The thermodynamic analysis indicated that the sorption process was endothermic for eosin Y and exothermic for red X and the negative value of change in Gibbs free energy indicated feasible and spontaneous adsorption for both of dyes. The removal percentage of dyeswas about 90% (q e= 31.72 mgg-1) for eosin Y and 93.44 % for red X(qe=32.44mgg-1). Overall, the present findings suggest that this environmentally friendly, efficient and low-cost adsorbent is useful for the removal of eosin Y and red X from aqueous solution.

This paper is concerned with the use of artificial neural network and multiple linear regression (MLR) models for the prediction of three major water quality parameters in the Gaza wastewater treatment plant. The data sets used in this study consist of nine years and collected from Gaza wastewater treatment plant during monthly records. Treatment efficiency of the plant was determined by taking into account of influent input values of pH, temperature (T), biological oxygen demand (BOD), chemical oxygen demand (COD) and total dissolved solids (TSS) with effluent output values of BOD, COD and TSS. Performance of the model was compared via the parameters of root mean squared error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r). The suitable architecture of the neural network model is determined after several trial and error steps. Results showed that the artificial neural network (ANN) performance model was better than the MLR model. It was found that the ANN model could be employed successfully in estimating the BOD, COD and TSS in the outlet of Gaza wastewater treatment plant. Moreover, sensitive examination results showed that influent TSS and T parameters have more effect on BOD, COD and TSS predicting to other parameters.

The present work was concentrated to study the ability of nanofiltration membrane as a treatment method of algal colored wastewater discharge from Islamabad refinery, Kermanshah, Iran. The polyether sulfone nanofiltration membrane was modified with sodium dodecyl sulfate (SDS) as an anionic surfactant and applied for treatment of colored wastewater. Water contact angle Scanning electron microscopy (SEM) and were applied to characterization of prepared membranes. The pure water flux, relative flux reduction as a parameter that represents antifouling property of membrane and also dye rejection were studied by dead-end and cross-flow filtration system in the present research. The period of the filtration time was extended about 6 hours to evaluate the stability and flux reduction of membrane. The results indicated 23.26% flux reduction was observed for modified membrane that confirms the antifouling property of prepared membrane. The results demonstrated that the permeate was completely transparent (100% dye removal, 98.2% turbidity removal), and the pure water flux was enhanced for modified membrane to 27.21 (Kg/m².h). In the present research nanocomposite polymeric membrane are introduced as an appropriate option for the treatment of natural colored wastewater.

The creation of very pollute palm oil mill waste water has resulted in semiserious environmental hazards. The reason for the current study is to test the optimal removal of ammonia nitrogen (NH3-N) from palm oil mill waste water by filtration using inexpensive filters media in place of current methods, to remove ammonia nitrogen from palm oil mill effluent. A series of batch and column studies were conducted using a different particle size of limestone (4, 12 and 20 mm) at various filtration rates of 20 ml/min, 60 ml/min and 100 ml/min. An experimental model design was conducted using Central Composite Design (CCD) in Response Surface Methodology (RSM). RSM was used to calculate the outcomes of process variables and their role in reaching ideal conditions. Equilibrium isotherms in this study were evaluated using the Langmuir and Freundlich isotherm. Using statistical analysis, the NH3–N removal model proved to be very significant with very low probability values (0.0001). The column study showed that ideal NH3-N removal was attained using a lower flow rate and smaller sized limestone (LS). The ideal conditions found when using 4 mm limestone and a 20 ml/min flow rate. This resulted in 45.3% removal of NH3–N which was seen in the predicted model, and fit well with the laboratory results (45%). The adsorption isotherm data fit the Langmuir isotherm.