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.
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. 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.
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.
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