javad sarvarian

Today, the dependency between energy consumption and water use has become an important issue in pressurized irrigation networks. In addition to the problem of water scarcity, rising energy costs are also a challenge for the agricultural sector. The objective of this study was to investigate different strategies to reduce energy consumption in the pressurized irrigation network of Saleh Abad.

For this purpose, firstly, minor changes affecting energy consumption including change in diameter of lateral pipe were evaluated. In this level, evaluated scenarios were lateral pipe diameters in mm 63 and 75. Hydraulic analysis of irrigation network was calculated using WaterGems software and energy consumption in different pumping stations was calculated using energy audit. In the second level of evaluation, four scenarios were defined to reduce energy consumption in the irrigation networks by structural changes in the irrigation network based on energy audit strategies, critical point control, network sectoring and pumping station management. The scenarios were (i) network sectoring without changing the main canal line, (ii) change of main canal line to pipe line, (iii) implementation a new first pipe line for a part of the network that is irrigated by gravity water, and (iv) elimination of the secondary pumping station by increasing the primary pumping station head. In the scenarios i, ii and iii, the part of the irrigation network lands was determined that could be irrigated without the need for a secondary pumping station. In this scenarios, the energy saving was calculated in a new condition by varying the type or number of pumps in secondary pumping stations to provide upstream discharge and head requirements.

The results showed that according to the topographic conditions of the network, changing the diameter of the laterals had no effect on saving energy. Also, the results showed that by network sectoring and applying scenarios(i), (ii), and (iv), the irrigated area without the need for a secondary pumping station were 610.8, 1591.5 and 1621.8 hectares, the energy saving in secondary pumping station were 14.6, 46.1 and 47.4% and the total energy consumed in network were 3.9, 12.4 and 12.7%, respectively. In the scenario iv, the primary pumping station was optimized. The result indicated that if the pumps type of the primary pumping station were changed and the water reservoir was implemented in higher level, it would be possible to eliminate the secondary pumping stations. In this case, the total area network (2820 ha) was irrigated without the secondary pump station and the energy saving was 8.46%.

The results of this study showed that changing the diameter of the lateral pipe had no significant effect on the energy consumption of the network. Also, the results showed that network sectoring and changes in main pipeline, it is possible to save up to 3121 MWh in the annual energy consumption of the network. Therefore, it is suggested that, network sectoring based on the input pressure of hydrants and the use of gravity, in the design of irrigation and drainage networks, considered as an effective solution to reduce energy consumption.

Due to limited water resources in Iran, the optimal use of water resources and improvement of water use efficiency is necessary, especially in agriculture. In current work, cropping pattern optimization was carried out in Mehran Plain of Ilam Province based on water resources, cultivated area and biodiversity constraints using genetic algorithm. The optimization model was applied to three different scenarios based on a combination of different constraints. The results showed that the cropping pattern in 2016-17 was not optimal and the biodiversity index was low. The resulting profit and biodiversity in all scenarios are higher than the current situation in Mehran plain. The amount of profit increase in combinations one, two and three is 70, 101 and 132% higher than the profit of the existing cropping pattern, respectively, and in terms of biodiversity, the Shannon-Wiener criterion is more than twice as high as the Shannon-Wiener criterion in the existing simple cropping pattern in all scenarios. Wheat, canola, sesame and okra are strongly represented in most optimal cropping patterns. Wheat has the largest acreage and tomato and alfalfa and corn have the least acreage due to the low profit and high water consumption of these products. Crops such as corn, sesame, okra and cucumber are strongly represented in the optimal cropping patterns and can be used as alternatives to the current crops to increase agricultural profits.

In order to predict the magnitude, velocity, and form of the flood wave with respect to time, the flood routing methods are used. There are several ways to accomplish the flood routing which are divided into two general categories, including hydraulic and hydrological routing methods. Among the various hydrologic routing methods, the Muskingum- Cunge method has been applied more frequently because no need to calibration process of output hydrograph. The accuracy of this method depends on the choice of reference discharge and geometrical parameters. In this study, a new model was proposed for calculating the reference discharge based on the comparison of Muskingum- Cunge method and MIKE11 hydrodynamic model for Dinehvar River, in Kermanshah Province. The results showed that the output hydrograph obtained by the proposed reference discharge was in good agreement with the output of MIKE11 model so that for the 200 years flood hydrograph, the RMSE and E criteria values were equal to 1.64 m3s-1 and 0.99, respectively. Furthermore, comparison of the proposed relationship with other researcher’s findings showed that the proposed relation not only was a function of the peak discharge but also was a function of peak time and base time of the input hydrograph.

The optimal design of urban water distribution networks is a significant issue that has been of critical interest in the water industry for many years. The optimal design of the distribution network aims to find the best solution for transferring water from the reservoir to consumers at the lowest cost. In this study, optimization of the water distribution network (ZONE 1 of Ilam city, Iran) was performed using the fast messy genetic algorithms (FMGA) tool in the hydraulic model for three different pipe networks. Also, these networks were optimized by using a combination of EPANET and an in-house developed binary genetic algorithm in MATLAB. The costs of the optimized hydraulic networks of polyethylene and polypropylene pipes were lower, respectively, by 20.56 % and 52.94 % compared to the consulting company's original designs, while for the glass fiber reinforced plastic pipe (GRP) pipe network the cost increased by 12.61 %. Also, the results of a developed algorithm for polyethylene pipe decreased by 22.13 %.

For safe pass of flood flow from upstream to downstream of dams, the spillways are used. Among the various spillways, in stepped spillways, the flow is dissipated when passing through the structure. This will reduce the cost of downstream energy-dissipaters structures such as stilling basin. In addition to energy dissipation, the volume and cost of stepped spillways can affect its design. The best design of the stepped spillway is that the remaining energy and the volume and cost of the spillway will be minimized. Therefore, the stepped spillway design is a multi-objective optimization problem. In the present study, a two-objective simulation-optimization model based on the NSGA-II algorithm was used to minimize the possible remaining energy and spillway volume. The results showed with increasing the relative height of spillway stairs, the amount of remaining energy is increased initially and after reaching a peak point, it would be reduced to a constant value. From the perspective of volume and cost of spillway, by increasing the relative height of the spillway stairs, its volume is increased linearly and by increasing the spillway angle, its volume decreased. The results of multi-objective optimization model showed that in the current plan of Siah-Bisheh Dam spillway, the remaining energy and the volume of spillway criteria were considered well. So that 83.7 percent of the flow energy is dissipated.

Irregular groundwater consumption and not replacing it has reduced groundwater level in many of aquifers at Iran. So determination of suitable areas for artificial recharge is very important as a solution to this problem. The goal of this study is determination of artificial recharge basins location by fuzzy logic and then optimal design of these basins. The ratio of income to expense was used to selection of the basins best design. So, five height scenarios were defined. All of scenarios were simulated by HEC-HMS model and their income was determined as infiltrated water volume. Also, the expense of scenarios was calculated based on materials used in building basins. The results showed that the suitable areas for artificial recharge plan implementation are 0.86% very efficient and 4.7% efficient by fuzzy logic method. Also the best scenario is 2.5 m based on the benefit to cost ratio and 2m based on the benefits view.

Desertification is recognized as a main problem in the arid and semi-arid areas. Therefore, identification and prediction of the effective factors in development of desertification are very important for better management of these areas. The main purpose of this study was evaluating the accuracy of an artificial neural network model for predicting the desertification process and selects the most effective criteria on desertification in the Dehloran plain by using the Iranian model for desertification potential assessment (IMDPA). In IMDPA model, water and climatic were selected as effective factors in desertification. In this model, three indicators for climate criteria; annual precipitation, drought index (Standardized precipitation index; SPI and continued drought and for water criteria; ground water table depletion, sodium absorption ratio, Cl, electrical conductivity (EC) and total dissolved solids were evaluated. Each index was rated using of IMDPA model. Then desertification intensity and criteria maps were prepared using a geometric average for predicting period in ArcGIS®9.3. Final data were entered into neural network to predict. The results showed that the neural network model has a high efficiency for predicting the desertification process in the study area. The accuracy of the model was about 80% and mean square error (MSe) was less than one. In addition, the climate factor and the index of EC were found the most effective variables for predicting the desertification process. In 2015-2016 predicted the most important probable criteria affecting the intensity of desertification were climate and water with weighted average 2 (moderate in sub-class1, 2 and 3), 1.84 (moderate in sub-class 1and 2), respectively.