جستجوی مقالات مرتبط با کلیدواژه "بهره برداری بهینه" در نشریات گروه "مهندسی آب"

Today, with population growth, urbanization and expanding demands of societies, water resources management has received more attention than the past decades. Urban water management is mostly conducted by controlling and directing surface runoff through reducing the peak discharge and flood volume. The construction of storm detention ponds in urban drainage systems delays the time of peak flow and reduces the peak discharge. The performance of these ponds depends on not only their dimensions and their inlet/outlet structures, but also the procedure of operating these structures. In this study, coordinated operation of detention ponds is taken into consideration at Tehran’s East stormwater urban drainage system, through the gates optimal maneuver. To achieve this, harmony search algorithm (HS) was linked to the SWMM hydrological-hydraulic model to find optimal gate openings of inlet/outlet structures of ponds in order to minimize the volume of stormwater exceeding the hydraulic capacity of drainage network. The results for five extreme rainfall events at the studied catchment showed that the optimal coordinated operation of these ponds has improved the level of ponds performance in terms of network flooding compared to the current method of operation and flooding in the drainage network was reduced by 62% in average for the considered extreme rainfall events.

After construction of dams according to different needs, the most important issue addressed by the exploiters and researchers is the issue of optimum operation management of reservoirs. In recent years, various meta-heuristic optimization techniques have been used for the reservoir operation optimization. In this study, an improved version of Charged System Search algorithm is presented to enhance the exploration ability and escape from local optima. For this purpose, a special mutation operator is used and a Mutant Charged System Search algorithm is introduced. Then, the validation and evaluation of the efficiency of this new improved algorithm are compared with other well-known meta-heuristic algorithms in solving the mathematical benchmark optimization problems. The new method achieves optimal results for 17 functions out of 18 functions. The proposed algorithm is used to solve two benchmark problems of operations of multireservoir sestem, namely four- and ten- reservoir sestems. Accordingly, the best values are obtained 308.29 and 1194.23, respectively. Finally, the efficiency of the algorithm for optimizing the operation of the two-reservoir system (Gorgan-Rood basin dams) is evaluated using various performance evaluation criteria. The results show the proper performance of the CSS algorithm and its mutated version in solving the multi-reservoir operation optimization problems. According to the results, the best solution achieved by the proposed algorithm is 0.22. Also, this algorithm showed the best performance by providing 94.63% and 95.30% of total demands of Golestan- Voshmgir multi-reservoir system.

In recent decades, Metaheuristic algorithms have been applied successfully in various water resource engineering and management issue especially in optimal operation of reservoirs. In this paper, a model based on Symbiotic Organisms Search (SOS) algorithm has been developed for modeling optimal operation of complex multi-reservoirs systems. In the first step, the performance of the method was successfully assessed through several benchmark functions. Then SOS algorithm was then used to derive the optimal operation of four- and ten- reservoir systems. The results of SOS were compared with other developed evolutionary algorithms including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results showed that, for cases of four- and ten- reservoir systems, the best solutions achieved by the SOS were 308.8114 and 1190.0227, respectively. The results revealed that the SOS algorithm was the superior algorithm in optimal operation of multi-reservoirs systems.

Under water shortage conditions, it is necessary to exercise water consumption management practices in water distribution networks (WDN). Intermittent supply of water is one such practice that makes it possible to supply consumption nodal demands with the required pressure via water cutoff to some consumers during certain hours of the day. One of the most important issues that must be observed in this management practice is the equitable and uniform water distribution among the consumers. In the present study, uniformity in water distribution and minimum supply of water to all consumers are defined as justice and equity, respectively. Also, an optimization model has been developed to find an optimal intermittent supply schedule that ensures maximum number of demand nodes are supplied with water while the constraints on the operation of water distribution networks are also observed. To show the efficiency of the proposed model, it has been used in the Two-Loop distribution network under several different scenarios of water shortage. The optimization model has been solved using the honey bee mating optimization algorithm (HBMO) linked to the hydraulic simulator EPANET. The results obtained confirm the efficiency of the proposed model in achieving an optimal intermittent supply schedule. Moreover, the model is found capable of distributing the available water in an equitable and just manner among all the consumers even under severe water shoratges.

Recent years have witnessed a growing interest in the optimal operation of water distribution networks (WDNs) as a result of their enhanced quality and reliability with due regard for the economic limitations faced. For this purpose, the present study was designed and implemeted to realize the two objectives of minimizing disinfection costs and maximizing reliability of water quality in water distribution networks using the multi-objective form of ACO algorithm as the optimization method. Different system analysis methods based on DDSM (demand driven simulation method) and HDSM (head driven simulation method) were also employed to investigate and compare the effects of constant versus time-dependent chlorine injection and pump speed. Results indicated that, compared to the condition with constant parameters of chlorine injectuion and pump speed, great improvements could be achieved in enhancing the reliability of water quality and reducing disinfection costs by using variable speed pumps (vsp) and time-dependent chlorine injection regimes without the need for buster pumps. Comparison of the analytical methods employed also indicated that application of HDSM led to reduced chlorine injection costs and improved reliability of water quality.

Optimal operation of multi-reservoir systems is amongst the important problems in the field of water engineering which cannot be easily solved. Nowadays, water engineering experts are using Meta-heuristic algorithms to solve this problem due to the computational capabilities of these algorithms. Ant Colony Optimization algorithm is one of the algorithms which is based on foraging behavior of real ants. In this research, the capability of four different ant colony optimization algorithms, Ant System, Elitist Ant System, Ranked Ant system, and Max-Min Ant System has been tested to solve the four- and ten-reservoir benchmark systems. For each algorithm two different formulations are proposed; in the first formulation reservoir releases and in the second formulation reservoir storages are considered as decision variables of the problem. The results showed that the Ant Colony Optimization algorithm is a competent algorithm to solve Multi-reservoir system operation optimization problem. The Max-Min Ant System is also assessed as superior to other algorithms for such problems.

The increasing demand for water in developed regions has led to numerous water supply challenges. Given their sensitivity and strategic importance as zones of cargo and passenger transfer, international airports are no exception to this general rule. The potential dangers to ground subsidence on the runway as a result of excessive groundwater withdrawal warrant plans aimed at developing models for optimal groundwater withdrawal as a measure of sustainable use of aquifers. In this study, consumption and withdrawal data are used and future development plans of the Imam Khomeini International Airport are considered to develop a model for groundwater use within the region based on multi-objective programming. Considering the objectives of maximizing water supply and minimizing costs of water transmission to demand zones, the model was used to develop an optimal water allocation system for the region containing monthly groundwater withdrawal policies and graphical distribution plans. The results show that the operation instructions thus developed for each well need to be carefully executed in order to ensure its safe exploitation. Strict adherence to the instructions is expected to enhance water supply reliability and to reduce the associated costs. This approach can be tailored and scaled up for future planning horizons and is also replicable in other areas by developing the relevant objective functions and constraints.

Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research، application of multi-objective particle swarm optimization (MOPSO) in optimal operation of Bazoft reservoir with different objectives، including generating hydropower energy، supplying downstream demands (drinking، industry and agriculture)، recreation and flood control have been considered. In this regard، solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP) using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then، ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP) results have been compared. Results showed that the MOPSO algorithm with 0. 3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.