Large Scale Reservoirs System Operation Optimization: the Interior Search Algorithm (ISA) Approach

Reservoirs are built to provide a powerful tool to control and manage surface water resources in order to cover inconsistency between water resources and demands. Due to finite available water and the increasing demands for water especially in arid and semi-arid regions like Iran, reservoirs must be optimally operated in order to use water in the most efficient way. This study applies the Interior Search Algorithm (ISA) to solve large scale reservoirs system operation optimization problems. The ISA is a meta-heuristic algorithm inspired from a systematic methodology of architecture process and mirror work utilized by Persian designers for decoration. Unlike other meta-heuristic algorithms, the ISA just have one parameter to tune which is a great advantage. In this study the parameter of the ISA tuned automatically using a linear equation. A real-world one-reservoir operation problem (i.e. Karun-4) and two large scale benchmark problems (i.e. four-reservoir and ten-reservoir operation problem) were employed to show the effectiveness of the ISA. The results shows the high ability of the ISA to solve reservoirs system operation problems as it achieved solutions 99.97, 99.99 and 99.95 % of global optimum for Karun-4 reservoir, four-reservoir and ten-reservoir system operation problems, respectively. These results are the best results reported so far in the studied problems. Comparing results of the ISA with those of non-linear programming (NLP), linear programming (LP), genetic algorithm (GA) and other meta-heuristic algorithms indicates fast convergence to global optimum. Considering the results, it can be stated that the ISA is a powerful tool to optimize complex large scale reservoir system operation problems.     

Optimal Inter-Basin Water Allocation Using Crisp and Fuzzy Shapley Games

In recent years, uneven distribution of available water resources as well as increasing water demands and overexploiting the water resources have brought severe need for transferring water from basins having sufficient water to basins facing water shortages. Therefore, optimal allocation of shared water resources in water transfer projects, considering the utilities of different stakeholders, physical limitations of the system and socioeconomic criteria is an important task. In this paper, a new methodology based on crisp and fuzzy Shapley games is developed for optimal allocation of inter-basin water resources. In the proposed methodology, initial water allocations are obtained using an optimization model considering an equity criterion. In the second step, the stakeholders form crisp coalitions to increase the total net benefit of the system as well as their own benefits and a crisp Shapley Value game is used to reallocate the benefits produced in the crisp coalitions. Lastly, to provide maximum total net benefit, fuzzy coalitions are constituted and the participation rates of water users to fuzzy coalitions are optimized. Then, the total net benefit is reallocated to water users in a rational and equitable way using Fuzzy Shapley Value game. The effectiveness of this method is examined by applying it to a large scale case study of water transfer from the Karoon river basin in southern Iran to the Rafsanjan plain in central Iran.     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox

Water Resources Management - We present a framework and toolbox for multi-model (one at a time) nonstationary modeling of rainfall-runoff (RR) transformation. The designed time-varying nature of...     

Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

Composition and biological activities of essential oils from four Heracleum species

The composition of essential oils from aerial parts of Heracleum persicum, a widely used medicinal plant, and three other Heracleum species growing wild in Iran were analysed by GC and GC–MS. Myristicin (53.6%), (E)-anethole (25.0%), hexyl butanoate (29.7%) and elemicin (41.1%) were the major compounds of Heracleum pastinacifolium, H. persicum, Heracleum rechingeri and Heracleum transcaucasicum, respectively. Cytotoxic activity assessed on three human cancer cell lines (HeLa, LS180 and Raji), showed that essential oils from H. transcaucasicum (IC₅₀ values; 0.362–0.594 mg/ml) followed by H. pastinacifolium (0.497–1.398 mg/ml) had moderate antitumoral activities. In the DPPH radical scavenging assay, H. pastinacifolium and H. persicum oils showed the highest activities with IC₅₀ values of 7.3 and 7.4 mg/ml, respectively. Antioxidant activity correlated well with the total phenolic content of the oils. None of the essential oils showed significant antimicrobial activities.     

Success criteria analysis in support of probabilistic risk assessment for nuclear power plants: application on SGTR accident

Success criteria analysis (SCA) bridges the gap between deterministic and probabilistic approaches for risk assessment of complex systems.To develop a risk model,SCA evaluates systems behaviour in response to postulated accidents using deterministic approach to provide required information for the probabilistic model.A systematic framework is proposed in this article for extracting the front line systems success criteria.In this regard,available approaches are critically reviewed and technical challenges are discussed.Application of the proposed methodology is demonstrated on a typical Westinghouse-type nuclear power plant.Steam generator tube rupture is selected as the postulated accident.The methodology is comprehensive and general;therefore,it can be implemented on the other types of plants and complex systems.     

Cube-octameric silsesquioxane (POSS)-capped magnetic iron oxide nanoparticles for the efficient removal of methylene blue

Octavinyl polyhedral oligomeric silsesquioxane (POSS) was polymerized on the surface of Fe3O4 nanoparticles (NPs) and then the NPs were functionalized with carboxylic acid groups using thiol-ene click reactions with thioglycolic acid.The as-prepared Fe3O4@POSS-COOH magnetic hybrid NPs had mesoporous structures with an average particle diameter of 15 nm and a relatively high specific surface area of 447 m^2· g^-1.Experimental results showed that 4 mg of Fe3O4@POSS-COOH NPs efficiently adsorbed and removed methylene blue from water at 5 min.This is due to the presence of both carboxylic acid and pendant vinyl groups on the Fe3O4@POSS-COOH NPs.These NPs could be easily withdrawn from water within a few seconds under moderate magnetic field and showed high stability in acid and alkaline aqueous mediums.     

Risk-constrained self-scheduling of a fuel and emission constrained power producer using rolling window procedure

This work addresses a relevant methodology for self-scheduling of a price-taker fuel and emission constrained power producer in day-ahead correlated energy, spinning reserve and fuel markets to achieve a trade-off between the expected profit and the risk versus different risk levels based on Markowitz’s seminal work in the area of portfolio selection. Here, a set of uncertainties including price forecasting errors and available fuel uncertainty are considered. The latter uncertainty arises because of uncertainties in being called for reserve deployment in the spinning reserve market and availability of power plant. To tackle the price forecasting errors, variances of energy, spinning reserve and fuel prices along with their covariances which are due to markets correlation are taken into account using relevant historical data. In order to tackle available fuel uncertainty, a framework for self-scheduling referred to as rolling window is proposed. This risk-constrained self-scheduling framework is therefore formulated and solved as a mixed-integer non-linear programming problem. Furthermore, numerical results for a case study are discussed.     

Low voltage low noise open loop automatic amplitude control for voltage-controlled oscillators

This paper presents a low voltage low noise open loop automatic amplitude control method for voltage-controlled oscillators (VCO’s). In this method a feedback mechanism keeps the VCO at its optimum amplitude over temperature and process variations and then the loop is broken to avoid noise injection form the control circuitry to the VCO. The loop does not add extra noise to the VCO. Based on the proposed method, a low voltage low noise LC-VCO was designed for a low phase noise application in TSMC 0.18 micron RFCMOS technology. Simulations show considerable improvement in the phase noise with the application of the proposed method.