Application of a Coordination Model for a Large Number of Stakeholders with a New Game Theory Model

Water Resources Management - Water allocation is an important issue for systems with multiple stakeholders. Individual and collective decisions are very important for such systems. Thus, a new...     

Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies

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.

     

Investigating various effects of reformer gas enrichment on a natural gas-fueled HCCI combustion engine

Homogenous charge compression ignition (HCCI) combustion has the potential to work with high thermal efficiency, low fuel consumption, and extremely low NO_x-PM emissions. In this study, zero-dimensional single-zone and quasi-dimensional multi-zone detailed chemical kinetics models were developed to predict and control an HCCI combustion engine fueled with a natural gas and reformer gas (RG) blend. The model was validated through experiments performed with a modified single-cylinder CFR engine. Both models were able to acceptably predict combustion initiation. The result shows that the chemical and thermodynamic effects of RG blending advance the start of combustion (SOC), whereas dilution retards SOC. In addition, the chemical effect was stronger than the dilution effect, which was in turn stronger than the thermal effect. Furthermore, it was found that the strength of the chemical effect was mainly dependent on H₂ content in RG. Moreover, the amount of RG and concentration of species (CO–H₂) were varied across a wide range of values to investigate their effects on the combustion behavior in an HCCI engine. It was found that the H₂ concentration in RG has a more significant effect on SOC at lower RG percentages in comparison with the CO concentration. However, in higher RG percentages, the CO mass concentration becomes more effective than H₂ in altering SOC.     

Optimization of energy management and conversion in the water systems based on evolutionary algorithms

Neural Computing and Applications - In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm...     

Potential of aqueous extract of <Emphasis Type="Italic">Hibiscus sabdariffa</Emphasis> leaves for inhibiting the corrosion of aluminum in alkaline solutions

Chemical (HE and WL) and electrochemical (PDP and EIS) measurements were applied to evaluate the potential of aqueous extract of Hibiscus sabdariffa leaves (AEHSL) for inhibiting the corrosion of Al in 0.5 M NaOH. It was found that the inhibition efficiency increases with the increase of AEHSL concentration. Electrochemical measurements revealed that AEHSL acts as a mixed-type inhibitor with inhibition category that belongs to geometric blocking. Adsorption of inhibitor species was found to follow Langmuir and Dubinin–Radushkevich isotherm models and the ability of AEHSL species to be adsorbed physically on the Al surface was illustrated by Dubinin–Radushkevich isotherm parameters. The data obtained from chemical and electrochemical measurements are in reasonably good agreement. Physical adsorption mechanism of AEHSl species on Al surface in 0.5 M NaOH becomes clear cut by following the trend of inhibitor adsorption with solution temperature. Good correlation between AEHSL water-soluble constituents and the suggested physical adsorption mechanism was obtained. Moreover, at a certain concentration of AEHSL (1.00 g L⁻¹), the Al surface coverage increases with the increase of NaOH concentration up to 0.5 M after which limited decrease was obtained with further increase in NaOH concentration.     

Hydrogen evolution assisted electrodeposition of bimetallic 3D nano/micro-porous PtPd films and their electrocatalytic performance

Self-supporting PtPd bimetallic catalysts with three-dimensional (3D) porous structures and a greatly enhanced surface area are firstly fabricated at a glassy carbon electrode (GCE) by a one-step strategy of potentiostatic co-electrodeposition utilizing hydrogen bubble dynamic templates. The atomic ratio of Pt/Pd in the bimetallic catalysts is varied by changing the composition of the electrodeposition solution. The 3D porous PtPd films are characterized by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) and examined as electrocatalysts for the electro-oxidation of methanol using cyclic voltammetry (CV), chronoamperometry and electrochemical impedance spectroscopy (EIS). Experimental results demonstrate that a small amount of Pd plays the predominant role in the formation of 3D porous structure for PtPd bimetallic catalysts and is an excellent catalytically enhancing agent for the Pt catalyst towards methanol electro-oxidation. The study on electrocatalytic performance of mono and bimetallic catalysts towards formic acid electro-oxidation also reveals the better activity of 3D porous Pd film for this reaction.     

Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional algorithms. The SMLA attained high Reliability and Resilience (Rel. = 0.98%, Res. = 50%) and minimum Vulnerability (Vul. = 21.9 of demand). It is demonstrated that shark machine learning algorithm would be a promising tool in handling the long-term optimization problem in operation a reservoir system.     

Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models

The study applies kidney algorithm for the optimization of reservoir operation for hydropower generation. The objective function defined for optimization is to minimize the hydroelectric power deficiency. Results of kidney algorithm are compared with those of bat algorithm (BA), water cycle algorithm (WCA), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSOA), and scatter matters search algorithm (SMSA). All algorithms are evaluated by Complex proportional assessment (COPRAS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), modified TOPSIS, and Weighted Aggregated Sum Product Assessment (WASPAS), as well as Borda count social choice theory. Then, vulnerability, time and volumetric reliability, as well as resiliency indices are used for comparison and multi-criteria decision-making indicators for selecting the best algorithm. It is found that no algorithm is ranked uniformly the best. Results indicate that kidney and particle swarm algorithms are ranked higher than other algorithms by most indices. Results of 10 random implementations of the objective function indicate that KA has a lower coefficient of variation and is computationally moe efficient. Further, most of the multi-criteria decision making models allocate the first rank to KA.     

Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm

This article shows an application of a new algorithm, called kidney algorithm, for reservoir operation which employs three different operators, namely filtration, secretion, and excretion that lead to faster convergence and more accurate solutions. The kidney algorithm (KA) was used for generating the optimal operation of a reservoir namely; Aydoghmoush dam in eastern Azerbaijan province in Iran whose purpose was to decrease irrigation deficit downstream of the dam. Results from the algorithm were compared with those by other evolutionary algorithms, including bat (BA), genetic (GA), particle swarm (PSO), shark (SA), and weed algorithms (WA). The results showed that the kidney algorithm provided the best performance against the other evolutionary algorithms. For example, the computational time for the KA was 3 s, 2 s, 4 s, 6 s and 3 s less than BA, SA, GA PSA and WA, respectively. Also, the objective function for the optimization problem was the minimization of the irrigation deficits and its value for the KA was 55%, 28%, 52%, 44 and 54% less than GA, SA, WA, BA and PSA, respectively. Also, the different performance indexes showed the superiority of the KA compared to the other algorithms. For example, the root mean square error for the KA was 74%, 61%, 68%, 33 and 54% less than GA, SA, WA, BA and PSA, respectively. Different multi criteria decision models were used to select the best models. The results showed that the KA achieved the first rank for the optimization problem and thus, it shows a high potential to be applied for different problems in the field of water resources management.     

A New Method for Flood Routing Utilizing Four-Parameter Nonlinear Muskingum and Shark Algorithm

Water Resources Management - Considering the great importance of flood prediction, flood routing based on Shark Algorithm (SA) and Four-Parameter Nonlinear Muskingum (FPNM) has been proposed in the...