The main purpose of the presented research is to raise the efficiency of pumping stations’ operational work by developing a model for reducing energy costs in urban water supply systems. Pumping systems are responsible for a significant portion of the total electrical energy use. Significant opportunities exist to reduce the pumping energy through smart design, retrofitting, and operating practices. Today, considering the increase in pumping energy prices in water conveyance systems, the problem of optimal operation of pumping stations is very actual. The optimal operation of pumping stations was determined using a Genetic Algorithm Optimization (GAO) to achieve the minimum energy cost. The paper presents a novel management model for the optimal design and operation of water pumping systems on a real case study for the town of Gonabad in Iran. To achieve this goal, three days in a year were selected randomly. The results indicate that the proposed mode in conjunction with a GAO is a versatile management model for the design and operation of the real pumping station. Modeling results show that optimization with a GAO reduces power consumption by about 15–20%.

Due to the complexity of calculating the minimum required volume of water tanks and the associated regime of pumping water into the tank depending on the consumption pattern in the water supply systems, finding the functional dependence of these variables is a complex process. The main idea of this paper was to provide a methodology for the calculation of the minimum water tank volume considering all input variables, which could be used in a simple and applicable way in everyday water supply management and engineering. As a final product, a desktop application TankOPT was developed that is easy to run and use on a PC with a user-friendly interface for data entry (data on maximum daily consumption and the pattern of daily water consumption). A software solution was created based on a numerical model that simplifies the usual manual calculations using known spreadsheet software and solves this problem. The solution was determined with combinations of the start and duration of water pumping in the water tank, for which the minimum required volume of the tank is obtained. JavaScript programming language was used to create the app. The use and operation of the application are shown through two hypothetical examples.

The adverse effects of improper disposal of collected and treated wastewater have become inevitable. To achieve the desired environmental standards, in addition to the construction of wastewater treatment plants, there is also a need to evaluate the continuous performance of treatment systems. In Iran, treated wastewater is mostly used in agriculture. Therefore, the use of wastewater with poor quality characteristics can endanger health. In this study, the neural network model's efficiency was investigated to predict the performance of the Perkandabad wastewater treatment plant in Mashhad in Iran. To achieve this, first, the factors affecting the TBOD parameter were identified as one of the quality indicators of the effluent. In the next step, using a genetic algorithm and network input factors, the performance of the treatment plant was predicted and evaluated. The highest correlation coefficient for the TBOD parameter was 0.89%. The results show that among the input parameters in the model, the amount of organic matter pollution load has the greatest effect on this prediction.

Water is an important factor in human health and an essential ingredient of living organisms. The increase of population and living standard has led to the increased water consumption consequently causing the increase of wastewaters, as well as bigger quality impairment. Wastewater treatment of the public mixed drainage system of the city of Čakovec (Croatia) and surrounding suburban settlements is carried out by mechanical and biological procedures, with the final treatment of separated sludge. In this paper we analyzed the input and output values of annual time series for chemical oxygen demand (COD) on the wastewater treatment plant in 2015 using the RAPS method (Rescaled Adjusted Partial Sums). The results showed that the input series contained more pronounced subseries with respect to their mean values and trends of increase and decrease, respectively. When comparing the input and output subseries, the output subseries do not oscillate to a large extent given that they express the output quality of wastewater. A significant reduction in the output values of the indicators determines the quality treatment of incoming wastewater.

The adverse effects of improper disposal of collected and treated wastewater have become inevitable. In order to achieve the desired environmental standards, in addition to the construction of a wastewater treatment plant, there is also a need to evaluate the continuous performance of treatment systems. In Iran, treated wastewater is mostly used in agriculture. Therefore, the use of wastewater with poor quality characteristics can endanger health. In this study, the efficiency of the neural network model in order to predict the performance of the Parkandabad waste water treatment plant in Mashhad, with a semi-mechanical treatment system, was investigated. The first step in predicting the performance of the treatment plant was identification of factors affecting the Total Biochemical Oxygen Demand (TBOD) parameter which is one of the quality indicators of the effluent. In the next step, the neural network model optimized with a genetic algorithm, and effective features as network inputs was used for the predictions of the performance of the treatment plant. Based on the results obtained from the model, the parameters that affect the prediction of TBOD concentration the mostwere singled out and they are flow rate, organic matter load, dissolved oxygen concentration, temperature, and some active aerators. Paper will consider replacing the semi-mechanical treatment system with the activated sludge process.

Due to the actual trends of the rising numbers of the population, as well as increasing of the living standard, wastewater treatment plants are exposed to the changes in the quantity and quality of input wastewater. Such changes the efficiency of the operational work of the wastewater treatment plant. There are many input and output parameters of the wastewater quality indicators (Biochemical Oxygen Demand through 5 days, Total Suspended Solids, Chemical Oxygen Demand, etc.), as well as input and output hydraulic parameters (flow of wastewater). There is a need to consider all of them and make a decision about the efficiency of the wastewater treatment plant. Among all procedures and methods, a multicriteria decision is one that could be applied in this research. The Paper will present the application of the Multi Composite Programming and Promethee method for the real case study of Parkandabad water treatment plant in Mashhad, Iran.

Sludge is one of the by-products of wastewater treatment plants (WWTPs). To assist biological processes, part of the produced sludge is returned to the treatment process and the excess is removed from the treatment plant whereby it undergoes thickening and dewatering. Numerous studies have been conducted to investigate the effect of different coagulants on dewatering from sludge, but the effect of using divalent iron for this purpose has not been investigated so far. In this research, the effect of divalent and trivalent iron compounds (ferrous sulfate and ferric chloride) on the dewatering of excess sludge of the first module of the Bojnourd WWTP (Iran) has been investigated. In this regard, first, the effect of different doses of each coagulant to optimize the dewatering characteristics of sludge was investigated and then the effect of pH change by adding lime was investigated. The results showed that the addition of optimal doses of FeSO4 (0.6 and 0.4 g/l) and lime (0.664 and 1.5866 g/l) reduced the capillary suction time of sludge by 30.6 and 32.7%, respectively, while reducing the moisture content of sludge cake by 26 and 30.6%.

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