مهدی دینی

In the present paper, the uncertainty analysis for the activated sludge part in the wastewater treatment plant (WWTP) was done using stochastic differential equation (SDE) equations. Euler-Maruyama method was selected for the numerical solution of the It ̂o integral and the results were compared and analyzed with observational data.

In this article, the long-term hydraulic performance of water distribution networks in peak consumption conditions is investigated by applying the uncertainty of model parameters and leakage variation. Applying the uncertainty of the leakage parameter and defining network operation planning based on leakage variation are research innovations. For this purpose, first, the model parameters are estimated in deterministic conditions in the desired years. Then, their non-deterministic values are calculated using statistical distributions and Monte Carlo method and applied to the model. Finally, the network performance levels are determined probabilistically. The Nodal Pressure Reliability Index (NPRI) is used to calculate the hydraulic performance of the network by coding in MATLAB linked with the EPANET simulator. The proposed method has been applied to a sample and real network. The results showed that by implementing the operational planning, the network performance increased by 15.3% in the middle year of the operation period, compared to non-operational planning. In general, by applying operational planning, the network's life increases in peak consumption conditions in terms of hydraulic performance.

Due to the widespread use of computers and measuring equipment in the operation of water distribution networks, a large amount of data is recorded for monitoring and evaluating the performance of water distribution networks and it is used in the modeling and calibration process. The management of these data is very necessary to achieve more accurate models on the one hand and the speed of their processing on the other hand. In this research, the purpose is to process nodal pressure field data to select the best statistical indicators for calibrating the water distribution network model. For this purpose, more than 5500 data collected in 22 stations of Ahar water distribution network and 12 stations of Oshnaviyeh water distribution network have been analyzed. First, by categorizing the data with Sturges experimental method, the probability of the data being placed in the central index categories of average, median, and mode and other categories in different stations in the times of minimum, maximum, and average consumption has been determined, and by summarizing the results, the best central index has been selected. Then, to analyze how the data changes, the minimum and maximum values, the range of variation, and the standard deviation of the data are presented along with the histogram of the categories. The trend of data variations in different stations in the minimum, maximum, and average consumption times shows that there is no specific harmony for data variations, so the maximum or minimum values of the range of variation and the standard deviation of the data are moved spatially in the stations. Also, the process of data allocation to categories shows that in the Ahar water distribution network, most data is allocated to the mode category at about 28.6 percent, followed by other categories at about 26.3 percent. Also, in the Oshnaviyeh water distribution network, the highest allocation is related to other categories with about 30.2 percent, followed by the mode category with 27.2 percent. Considering the multiplicity and dispersion of other categories and the unity of the mode category, the mode category is the best choice for both case studies. In general, by using mode values instead of other central indicators in the calibration of water distribution networks, due to the effectiveness of more field data, more favorable results will be obtained in the construction of the network model.

Given that, observational data have different uncertainties, the calibration of water distribution networks based on a definite value can affect the results of the calibrated model. In this research, the calibration of water distribution networks by considering the uncertainty of nodal pressure has been investigated. For this purpose, first, by generating a series of nodal pressure data using the Monte Carlo method, the Hazen-Williams coefficients of the network have been obtained. Then, by producing a series of Hazen-Williams coefficients, the nodal pressure values have been calculated and the results of the two pressures have been compared. The calibration model was performed using the particle swarm optimization algorithm by linking the EPANET simulator in MATLAB. The proposed method has been implemented on the two-loop sample network and real water distribution network of Sufian city. The results show that the application of node pressure uncertainty in network nodes leads to multiple sets of solutions for Hazen-Williams coefficients of pipes with different variation ranges and standard deviations, which according to the average absolute error of 4 and 3.8 percent between the average nodal pressure in the sample and the real network in the non-deterministic mode with the deterministic mode, the proposed method, can be a more accurate method for calibrating networks. However, it should be noted that there are challenges in the modeling and execution time of calibration models.

Generally, the modeling of the Water Distribution Networks (WDNs) is done by the conventional methods that cause the outputs of the network are normally deterministic value by assuming certain inputs and parameters. But, in real ones, there are many uncertainties in model parameters. which causes, the results obtained by the conventional methods may not be satisfactory in practice, Therefore, the variation of key parameters in WDNs such as pipe roughness and diameter and also nodal demand can change nodal pressure and pipe flow that affected the performance of the network. However, by understanding the parameter uncertainties and how the uncertainties affect the accuracy of the model, the decision-makers can make the best decision that can prevent the WDNs from the unreliable events.

Due to the particular topography of Kaleybar city, the water distribution network of this city has 7 pressure reducing valves that have been experimentally installed in different locations to manage the extra pressure in the network. In this research, the effect of the intelligent emplacement of pressure-reducing valves is investigated by using the valve selection index to reduce leakage and increase network reliability. The aim is to minimize hourly leakage by determining the optimal location and setting of valves, In addition to the minimization of leakage as the objective function, the minimum pressure restriction in three cases of no pressure restriction (minimum pressure restriction equal to 0 meters), water supply at the entrance of the building (minimum pressure restriction equal to 10 meters) and water supply for a four-story building ( minimum pressure constraint equal to 26 m) is considered as a penalty function next to the objective function. For this purpose, a combination of the ant colony optimization algorithm with the EPANET simulator in MATLAB has been used. The results show that with the intelligent emplacement of pressure-reducing valves in Kaleybar City, the leakage of the network has been decreased by 15.9 percent and the reliability of the network has been increased by 15. 5 percent, which shows the effect of intelligent operation planning on water distribution networks in improving their hydraulic performance.

In this paper, the uncertainty of artificial intelligence models for evaluting performance of the activated sludge unit of the Tabriz treatment plant is assessed. In this regard, daily data of pollution parameters, particularly Biochemical Oxygen Demand and Chemical Oxygen Demand, are utilized. All data were collected daily during the years (2015-2020) and the best parameters were selected using the correlation coefficient criterion. The TSSi, TDSi, VSSi, pHi parameters and also, BODe and CODe with a one-day delay were selected as model input and BODe and CODe were selected as model output. The calculations of uncertainties were performed in two models of Feed Forward Neural Network as point prediction and lower upper bound estimation method to provide the Prediction Interval. The LUBE method, unlike the classical methods of calculating PI, estimates PI without the need for data distribution information. In this method, the FFNN was trained with two outputs indicating the upper and lower limits of the prediction. PICP assessment and comparing it with μ values, caused γ values to equal zero that, in the continuation of the calculation process caused CWC extraction with the minimum possible amount and production of PI for computational data and observations with the possibility of controlling random changes in the activated sludge section. So, the convergence of the LUBE method has the ability to effectively control the uncertainty between the parameters of the biological section of activated sludge using PI. The time required to build PI is considerably short. Numerical results show approximately 99% success in calculations and coverage of modeling uncertainties. Providing an oscillating range of uncertainties can be a valuable aid in improving economic conditions as well as reducing activated sludge control time and better treatment plant monitoring. Despite the design criteria for BODe of 20 mg per liter, PI results show a supply of 12% of the design index. However, considering the supply of the remaining 88% in terms of quality standard for the use of effluents and returned water, according to the Deputy of Strategic Supervision, publication 535, at the rate of 31 mg per liter in the activated sludge sector, the proper performance of the treatment plant is demonstrated. The LUBE method is an efficient method, so by providing an optimized range of fluctuations for computational data, the smallest abnormal changes in the activated sludge section due to controlling the amount of food for the micro-organisms present in this section; also, the pollution indicators with the least computing time are also reported. In addition, due to the high cost of activated sludge in the wastewater treatment sector, from an economic point of view, it also helps reduce costs. According to the non-linear behavior of bacteria during the reduction of food, as well as the control of mortality caused by the reduction of food, it can be considered a very effective tool.

Now there are various models to simulate rainfall runoff. WMS and HEC-HMS models are comprehensive models that by incorporating GIS features can be a powerful tool in the modeling and management of catchment area. The aim of this paper is to estimate maximum flood of Namin catchment at the SCS method with using WMS and GIS by applying probability distributions. First, digital elevation model and catchment waterways network was drawn by using GIS. Then, based on land use maps and properties of soil permeability, catchment store coefficient maps in format of the SCS were obtained and by using WMS software for different return periods of 2, 5, 10, 25, 50 and 100 years, 6 hour rainfall and maximum flood of catchment estimates and are compared with real results. Normal, Gumball and Pearson distributions are used to complete data deficits. Results show that the 6-hours rainfall of the catchment is between 13.7 and 30.1 mm and the maximum flood of the catchment is between 0.2 and 9 m3/s. Comparison of the results show that the log Pearson III distribution has the best fit for real data and the mean relative error for 100 years return period is 0.8 percent that confirm the accuracy of the model.

Control of water leakage in Water Distribution Networks (WDNs) is important to save drinking water consumption. Pressure management is one of the solutions that directly affected the water leakage and reliability of the WDNs. The leakage rate depended on the nodal pressure, high-pressure increases the leakage rate and failure in the network, and low pressure decreases water supply reliability in WDNs. However, it is necessary to be preserved sufficient pressure throughout the network to ensure that consumer demands are fully provided at all times. Pressure management is one of the effective and cheapest methods for leakage reduction and reliability addition in WDNs. The aim of this paper is to develop a methodology based on maximizing the network reliability by applying an optimized scheduling program for setting flow control valves (FCVs) and pumps at different times of the day, leading to pressure management and leakage reduction in the networks. For this purpose, the particle swarm optimization (PSO) algorithm is used to find the optimal setting of FCVs and pumps. The aim of the optimization algorithm is to maximize the network reliability of the WDN. For this purpose, the network pressure reliability index (NPRI) is used which is proposed by Dini and Tabesh (2017). Also, to determine the optimal location of the valves, the valve selection index (SI) is used which is proposed by Ali (2015). The methodology is verified by an example network (Jowitt and Xu, 1990). Also, it is applied to an example network and Ahar WDN.

In recent decades, due to the limitations of water resources, much research has been done in relation to the optimal operation of water distribution networks. In this regard, the use of appropriate methods to control and set the performance of pumps, valves, reservoirs and tanks in these systems are of considerable importance. In this paper, the aim is to regulate the optimal number and speed of pumps in pumping stations to maximize the hydraulic performance of water distribution networks. For this purpose, a new index is presented based on the pump speed optimization process to determine the number of turn-on pumps. In addition, the Nodal Pressure Reliability Index (NPRI) is used to evaluate the hydraulic performance of the network. The hydraulic analysis of the network is performed using EPANET and the optimization process is performed using the Modified Standard Particle Swarm Optimization algorithm (MSPSO), both of which are done in MATLAB code. The proposed method has been implemented in the form of four scenarios on the Khomam water distribution network, Gilan province. Scenarios include current status with Single Speed Pumps (SSP), Best Setting of Rotational Speed Pumps (BSRSP), Best Number of Turn on Single Speed Pumps (BNTSSP) and Best Number and Speed of Turn on Rotational Speed Pumps (BNSTRSP). Comparison of the results shows that the BSRSP and BNSTRSP scenarios with the slightest difference have the highest reliability and the lowest leakage. So that they increase the reliability of the network by 66.10 and 66.06 percent, respectively, and reduce the leakage by 23.53 and 23.48 percent, respectively. However, in the BNSTRSP scenario, the number of turn-on pumps is 13.16 percent less than the BSRSP scenario. Also, in the BNTSSP scenario where the number of turn-on pumps is less than the SSP scenario, the reliability of the network increases 21.36 percent and the leakage rate decreases by 10.36 percent, which emphasizes the efficiency of the NTPI index in improving network performance. In general, determining the number of turn-on pumps in pumping stations and optimizing the speed of variable speed pumps has a significant effect on increasing the hydraulic performance of water distribution networks, which can be very effective in saving energy and water resources and increasing network performance.

Pressure management is an efficient and inexpensive way to reduce leakage in water distribution networks, which is usually possible by systematical operation of valves, pumps, reservoirs, and tanks. In water distribution networks, there are many valves only used during the repairs in the time of accidents. In this paper, a simple approach is proposed for managing pressure and leakage in networks with optimal scheduling of the existing valves’ operation. For this purpose, the optimal locations of the valves are determined by using the valve selection index and their opening rate has been optimized in the minimum, medium and maximum consumption conditions. The optimization was done by combining particle swarm and ant colony optimization algorithm and EPANET simulator within the framework of MATLAB. The proposed method is performed both on a sample and the real network. The results show that there is a significant improvement in the performance of both networks in the average and maximum consumption conditions, so that, the reliability of the Maragheh city network is improved by 22 and 24 percent and leakage by 31 and 20 percent, respectively, compared to the base model. In general, using this approach can improve the performance of real networks by manual programming of a number of selected valves.

Gradually, with the urban population growth and development of cities, water distribution networks (WDNs) gain significant importance. So the need for computerized modeling of WDNs is felt more than ever to understand the behavior of these systems. The most important problem with modeling of WDNs is consistency between the calculated and measured data. Setting the coefficients of the model via measured data is necessary. Model parameters include Hazen-Williams coefficients in the pipes, base demand and demand pattern coefficients at the nodes. The calculated and measured data mainly include pressure head at nodes, tank levels, and flow in pipes, that can be considered either in steady state or in an extended period condition. The aim of this paper is to investigate the performance of PSO algorithm for setting the Hazen Williams coefficients of WDN Models. For this purpose, five models of the PSO algorithm and three models of the ACO algorithm were made. The proposed method tested on a two-loop test example and a real water distribution network.

Regarding the complexity of WDNs, the need for computerized modelling of WDNs is felt more than ever for monitoring their performance at the operational stage. One of the most important issues in modelling is to adjust the results of modelling with the real status of the system. So it is necessary to calibrate the model by observed data. Finding a suitable method for adjusting the model's coefficients is one of the main challenges in computerized modelling. In this paper, a modified particle swarm method is presented to adjustment of water distribution network coefficients by modifying the velocity equation of the particle swarm and Combining it with the mutation operator. Thus by defining the coefficients of the models, four models such as standard particle swarm optimization model (SPSO), modified standard particle swarm optimization model (MSPSO), standard particle swarm optimization model with a mutation (SPSOM) and modified standard particle swarm optimization model with a mutation (MSPSOM) are constructed. The Rastrigin test function is used for verification of the modified particle swarm equation and the two-loop network is used for verification of models and also the four-loop network and real water distribution network are used for detailed analysis. The optimization is done in MATLAB by combining the particle swarm optimization algorithm and the EPANET software. Comparison of the results of the standard particle swarm model and the modified standard particle swarm model for the Rastrigin test function showed that modifying the particle swarm velocity equation increased the model's ability to determine the actual answers and reduced the costs. The MSPSOM model finds the optimal answer for two-loop and four-loop networks with a probability of 96.7 and 95 percent respectively. So it is the best model among all models in this criteria. Also, the MSPSO model finds the optimal answer for two-loop and four-loop networks in lowest time compared to other models. So it is the best model among all models in this criteria. Comparing the results of the models in the Ahar water distribution network showed that the modified standard particle swarm with the mutation model have the lowest minimum and average values of the modeling data error. So it has the best performance among the particle swarm models. In general, the correction of the particle swarm velocity equation in the form of the standard particle swarm model, and the correction of the particle swarm velocity and its integration with the mutation operator in the form of a modified standard particle swarm with a mutation has a higher ability to adjust the water distribution network coefficients.

Due to an intense loss in groundwater level and a high decrease in reservoir capacity, the Marand plain has been banned since 1991. However, since 1994, the groundwater level been dropping year by year. As a result, many wells and qanats have been dried or their discharge capacity has been decreased, which has caused a lot of problems for the operating organizations. In this study, to investigate and manage the groundwater level variation in the Marand plain, three scenarios including supplying all consumption from groundwater resources, supplying drinking and industry consumption from the Aras River, and supplying drinking and industry consumption from the Aras River and applying the optimal management of the consumption for agricultural uses were defined. The water balance equation was used to establish the relationship between input, output, storage in an aquifer, and a variation of groundwater level in the Marand plain was estimated based on the available data (2005-2014). Finally, the zoning of the groundwater level was done for the September 1398 and the results were compared with the September 1393. In this research, the Marand plain with an area of 562.22 km2 in the northwest of East Azerbaijan province was selected as a case study. Investigating the hydrological and meteorological parameters of the Marand plain between the years 1982 and 2013 showed that the average annual precipitation was 283 mm, the average annual temperature was 12.8°C, and the average annual pan evaporation was 104 mm. The aim of this research was to estimate the spatial and temporal variation of the groundwater level of the Marand plain. For this purpose, the water balance estimation was done by using Excel and the zoning of groundwater level variation was done by using ArcGIS. A groundwater level analysis of the Marand plain was carried out based on the 50 observations of wells during 2005 to 2014. In this period, according to the groundwater level data, the aquifer parameters, such as the loss of the groundwater level and the amount of water withdrawal from the aquifer were determined. Then, various scenarios were defined for assessing the status of the aquifer.
Result and The analysis of the groundwater level in the Marand Plain in a year statistical period (1982-2013) showed that the groundwater level of the plain had decreased about 16.56 m. Also the average annual groundwater level of the plain decreased about 48 cm between the years 2005 and 2015.
By applying the first scenario, in the next five years, the fall of the groundwater level in the zone 1 will be 2.35 m, in the zone 2 will be 2.25m, in the zone 3 will be 2.6m, and in the zone 4 will be 2.45m.
Also the area of the zone 4 had increased from 246 km2 to 252 km2 which indicated a further fall of the groundwater level in a large area of the plain. By applying the second scenario, in the next five years, the growth of the groundwater level in the zone 1 will be 1.74 m, in the zone 2 will be 1.67 m, in the zone 3 will be 1.93 m, and in the zone 4 will be 1.82 m. Indeed, following this trend, the groundwater level after 14 years will return to the situation of 10 years ago (2005). In addition, after 46 years, it will return to the situation of 32 years ago (1982). By applying the third scenario, in the next five years, the growth of the groundwater level in the zone 1 will be 4.78 m, in the zone 2 will be 4.58 m, in the zone 3 will be 5.29 m, and in the zone 4 will be 4.99 m. Following this trend, the groundwater level after 5 years will return to the situation of 10 years ago (2005). After 17 years, it will return to the situation of 32 years ago (1982). The aim of this research was to estimate the spatial and temporal variation of the groundwater level in the Marand plain. The results showed that the annual average groundwater level had decreased 48 cm/year between the years 2005 and 2014. Also by applying the first scenario, with an intensified decline in the groundwater level in all zones, the status of the Marand aquifer becomes more critical. For example, in some areas, the groundwater level will decease about 2.6 m. By applying the second scenario, the groundwater level will increase at least 1.67 m and utmost 1.93m and by following this trend, after 14 years, the aquifer will return to the situation of 2005. Also by applying the third scenario, the groundwater level will increase at least 4.78 m and utmost 5.29m and by following this trend, after 5 years, the aquifer will return to the situation of 2005.

Various reliability indices have been defined by researchers for the water distribution networks. Due to the complexity and heterogeneous nature of distribution networks, results of indicies can vary substantially and municipalities have hard time to select the most practical indicies for their networks. This study was undertaken to develop a new method for identifying the best reliability index by considering various factors including the appropriate sensitivity to changes in network parameters, the appropriate shift in extreme conditions, the capability in covering critical condition, hydraulic condition, and water quality reliability. To accomplish the objective of the study, several water distribution networks were evaluated under multiple scenarios and the optimum reliability indices were verified with an example real water distribution network in Ahar. Controlling parameters included in scenario analysis for network reliability indices include: minimum, maximum and optimal pressure, residual chlorine in nodes, velocity in pipes, nodal pressure reliability, pipe velocity reliability, and nodal residual chlorine reliability . To evaluate the performance of defined index, a two-loop test (critical) were compared with the conventional single-lop test (normal). The performance assessment of indices obtained from this study showed that the index values ranged between 0.11 and 1.0, which indicate the normality and the appropriate sensitivity of the indices. Also, by increasing the residual chlorine of the reservoir, the network reliability index changed from 0.54 to 0.75 and then as the residual chlorine decreases in the distributon network the reliability index decreased back to 0.54. This appropriate shift in reliability index while changing residual chlorine in the network, cearly shows the the effectiveness of new method in determining the reliability indicies under extreme conditions. Also, the new method did not respond to the failure of some network components, as were evident with insignificant change in the indices values under critical conditions. From the results of this study generally can be concluded that the newly defined relability indices is an efficient way for evaluating the performance of water distribution networks.

Gradually, with urban population growth and development of Ahar city, old houses in the lanes and alleys are changing into high-rise buildings. In most of these buildings the regulations are violated. This issue has created a lot of problems for the inhabitants. The goal of the paper is to answer this question: "what is the consequence of high rise building constructions without observing urban rules and regulations on hydraulic performance of the Ahar water distribution network?" To answer this question, Ahar water distribution network was selected as a case study and its hydraulic performances were investigated in different situations.

Due to the breadth and complexity of water distribution networks, modeling of these networks is essential for proper utilization and proper network management. Determination of network coefficients is the more importance in network modeling. Adjustable coefficients of the model include roughness coefficients of pipes and consumption in nodes. Measurable quantities also include pressure in the nodes and flow rate in the pipes.In a good model of the network, it is expected that the pressure and discharge values that are modeled with the values of pressure and discharge of the observations are equal or the least difference. This is done in the form of an optimization model and by setting the coefficients of the model. In this paper, the roughness coefficients of the tubes are determined by assuming that the values of pressure in the network nodes are known. This work is done in the form of an optimization model in MATLAB software. The particle algorithm is used to optimize. The optimization model is located on a relatively large sample network, i.e. the Gepson network, with 30 pipes calibrated.The results showed that the network in the case of non-categorizing the roughness coefficients of the network of pipes, has the optimal local solution with accuracy in the amount of the actual optimal solution. The network has categorized the coefficients of the actual optimal response. The results showed that the proper classification of network pipe coefficients was effective in determining the exact coefficients, especially for large networks.

In recent years many researches have been performed on the subject of optimal usage of water resources. One of the issues in this regard is preparing short term water demand forecasting in conjunction with the optimum water demand management. Considering the effects of climatologic conditions on the short term water demand and according to the similarity of consumption trends in the consecutive days, two conventional and advanced models have been developed in this paper using time series method. These models have been used to predict the short term water demand in Tehran, Iran. In the conventional model the time series of Tehran daily water consumption is divided into different components of trend, seasonal variations, and random variations which are obtained by regression method. In the advanced model the consumption pattern in the past is recognized using combined methods of Auto Regressive (AR) and Seasonal Moving Average. Assuming that this pattern will continue in the future, it is then applied to predict daily water demand. In the conventional models it is assumed that different components of time series are independent and dividable. Therefore, its components are determined by different methods such as regression, moving average, etc. In the advanced models all the components are supposed to be correlated to each other and are therefore analyzed together. Comparing the results with the real data showed the ability and accuracy of the both time series models to predict the Tehran daily water demand. The advanced models produced better results than the conventional methods.