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.
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