7 results
Search Results
2. Reliability-Based Simulation-Optimization Model for Multireservoir Hydropower Systems Operations: Khersan Experience.
- Author
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Afzali, Raheleh, Mousavi, Seyed Jamshid, and Ghaheri, Abbas
- Subjects
SIMULATION methods & models ,STREAMFLOW ,RESERVOIRS ,ALGORITHMS ,MATHEMATICAL optimization - Abstract
A common method for assessing the energy potential of hydropower systems in Iran is the sequential streamflow routing with control on the energy-yield reliability. The method results in developing a reliability-based simulation (RBS) model that is used to analyze single-reservoir hydropower systems. In most of the cascade hydropower systems in the country, a single-reservoir RBS model is usually employed in the design and operation of each of the systems reservoirs. This paper presents a multireservoir RBS model considering the integrated operation of the systems. The model employs the general algorithm of the single-reservoir RBS model; however, with a single-period optimization submodel in determining releases from reservoirs during the system simulation. The objective function of the submodels minimizes the sum of reservoir releases and/or maximizes the sum of reservoir storages in each of the time periods, subject to a reliability constraint on the system’s energy yield. The single and multireservoir RBS models are applied in the Khersan hydropower system located in Iran as a case study. The multireservoir RBS, compared to the single-reservoir model, results in 7.9% excess firm-energy yield in reliability of 90%. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
3. Neural-network-based simulation-optimization model for water allocation planning at basin scale.
- Author
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Shourian, M., Mousavi, S. Jamshid, Menhaj, M. B., and Jabbari, E.
- Subjects
ARTIFICIAL neural networks ,MATHEMATICAL optimization ,RIVERS ,NEURAL circuitry ,WATERSHEDS ,ALGORITHMS - Abstract
Heuristic search techniques are highly flexible, though they represent computationally intensive optimization methods that may require thousands of evaluations of expensive objective functions. This paper integrates MODSIM, a generalized river basin network flow model, a particle swarm optimization (PSO) algorithm and artificial neural networks into a modeling framework for optimum water allocations at basin scale. MODSIM is called in the PSO model to simulate a river basin system operation and to evaluate the fitness of each set of selected design and operational variables with respect to the model's objective function, which is the minimization of the system's design and operational cost. Since the direct incorporation of MODSIM into a PSO algorithm is computationally prohibitive, an ANN model as a meta-model is trained to approximate the MODSIM modeling tool. The resulting model is used in the problem of optimal design and operation of the upstream Sirvan river basin in Iran as a case study. The computational efficiency of the model makes it possible to analyze the model performance through changing its parameters so that better solutions are obtained compared to those of the original PSO-MODSIM model. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
4. Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.
- Author
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Banadkooki, Fatemeh Barzegari, Ehteram, Mohammad, Ahmed, Ali Najah, Teo, Fang Yenn, Ebrahimi, Mahboube, Fai, Chow Ming, Huang, Yuk Feng, and El-Shafie, Ahmed
- Subjects
SUSPENDED sediments ,MATHEMATICAL optimization ,FORECASTING ,ALGORITHMS ,ARTIFICIAL neural networks - Abstract
Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
5. Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies.
- Author
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Karami, Hojat, Farzin, Saeed, Jahangiri, Aylin, Ehteram, Mohammad, Kisi, Ozgur, and El-Shafie, Ahmed
- Subjects
PARTICLE swarm optimization ,MATHEMATICAL optimization ,WATER supply ,WATER shortages ,SEARCH algorithms ,ALGORITHMS - Abstract
The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacks – notably early convergence, a low rate of convergence, or trapping in local optimizations – that limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
6. A New Inversion Method Using a Modified Bat Algorithm for Analysis of Seismic Refraction Data in Dam Site Investigation.
- Author
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Poormirzaee, Rashed, Sarmady, Siamak, and Sharghi, Yusuf
- Subjects
EARTHQUAKE resistant design ,METAHEURISTIC algorithms ,SEISMIC refraction method ,MATHEMATICAL optimization ,EFFECT of earthquakes on dams ,SEISMIC wave velocity ,ALGORITHMS - Abstract
Similar to any other geophysical method, seismic refraction method faces non-uniqueness in the estimation of model parameters. Recently, different nonlinear seismic processing techniques have been introduced, particularly for seismic inversion. One of the recently developed metaheuristic algorithms is bat optimization algorithm (BA). Standard BA is usually quick at the exploitation of the solution, while its exploration ability is relatively poor. In order to improve exploration ability of BA, in the current study, a hybrid metaheuristic algorithm by inclusion a mutation operator into BA, socalled mutation based bat algorithm (MBA), is introduced to inversion of seismic refraction data. The efficiency and stability of the proposed inversion algorithm were tested on different synthetic cases. Finally, the MBA inversion algorithm was applied to a real dataset acquired from Leylanchay dam site at East-Azerbaijan province, Iran, to determine alluvium depth. Then, the performance of MBA on both synthetic and real datasets was compared with standard BA. Moreover, the dataset was further processed following a tomographic approach and the results were compared to the results of the proposed MBA inversion method. In general, the MBA inversion results were superior to standard BA inversion and results of MBA were in good agreement with available boreholes data and geological sections at the dam site. The analysis of the seismic data showed that the studied site comprises three distinct layers: a saturated alluvial, an unsaturated alluvial, and a dolomite bedrock. The measured seismic velocity across the dam site has a range of 400 to 3,500 m/s, with alluvium thickness ranging from 5 to 19 m. Findings showed that the proposed metaheuristic inversion framework is a simple, fast, and powerful tool for seismic data processing. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
7. A Novel Approach to the Gas-Lift Allocation Optimization Problem.
- Author
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Hamedi, H., Rashidi, F., and Khamehchi, E.
- Subjects
OIL well gas lift ,MATHEMATICAL optimization ,PARTICLE swarm optimization ,ALGORITHMS ,OIL fields ,PERFORMANCE - Abstract
Because compressed gas is a scarce and expensive resource, allocating an optimal amount of gas injection to a group of wells to increase the oil production rate is an important optimization problem in the gas lift operation. In this article, a particle swarm optimization algorithm is employed to assign an optimum gas injection rate for each individual well. Also, a new gas lift performance curve-fit that can reduce the time and volume of the computation is suggested. Finally, the algorithm is tested on five wells in an Iranian oil field. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
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