A hybrid evolutionary algorithm for distribution feeder reconfiguration

Distribution feeder reconfiguration (DFR) is formulated as a multi-objective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance (λ ₂ norm) between the vector-valued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.     

Stress test optimization using an integrated production test and field reliability model

Numerous papers and texts have been written in the reliability literature regarding the determination of the optimum test duration for a production stress or a burn‐in test. The techniques presented have largely been based on the identification of the change point at which infant mortality has largely been removed from the units. The time‐on‐test is typically the only factor that influences this decision. Few of these models have attempted to integrate the field performance or the influence of warranty costs into this decision. This paper proposes and validates a methodology that integrates the influence of the production test failures and the field performance including their respective costs into a single unified model. The objective is to identify a production test duration that minimizes the overall cost. A Weibull model is initially developed for the production test that incorporates the failure observations in different time segments of the test based on the ability to detect latent defects in the product. A separate Weibull model is then developed for the product's performance in the field that includes the lifetime of the unit. This paper identifies how both these Weibull models can be combined into a single model including both test and field costs with the objective of minimizing the overall cost. The advantage of the proposed technique is that it does not require one to track individual units from production through to the field in order to develop an integrated test and field cost model. Copyright © 2009 John Wiley & Sons, Ltd.     

Removal of chemical oxygen demand from textile wastewater using a natural coagulant

A biomaterial was successfully synthesized from Plantago ovata by using an FeCl₃-induced crude extract (FCE). The potential of FCE to act as a natural coagulant was tested for the pretreatment of real textile wastewater. Tests were performed to evaluate the effects of FCE quantity, salt concentration, and wastewater pH on chemical oxygen demand (COD) reduction during a coagulation/flocculation process. Experimental results indicated that the wastewater could be effectively treated by using a coagulation/flocculation process, where the BOD₅/COD ratio of the effluent was improved to 0.48. A low coagulant dose, 1.5mg/L, achieved a high COD removal percentage, 89%, at operational conditions of neutral pH and room temperature. The experimental data revealed that the maximum COD removal occurred at water pH<8. Increasing the salt promoted the COD removal. The settling and filterability characteristics of the sludge were also studied. Scanning electron microscopy and energy dispersive spectroscopy studies were conducted to determine the sludge structure and composition, respectively. Overall, FCE as an eco-friendly biomaterial was revealed to be a very efficient coagulant and a promising option for the removal of COD from wastewaters.     

Nitric oxide-releasing porous silicon nanoparticles

In this study, the ability of porous silicon nanoparticles (PSi NPs) to entrap and deliver nitric oxide (NO) as an effective antibacterial agent is tested against different Gram-positive and Gram-negative bacteria. NO was entrapped inside PSi NPs functionalized by means of the thermal hydrocarbonization (THC) process. Subsequent reduction of nitrite in the presence of d-glucose led to the production of large NO payloads without reducing the biocompatibility of the PSi NPs with mammalian cells. The resulting PSi NPs demonstrated sustained release of NO and showed remarkable antibacterial efficiency and anti-biofilm-forming properties. These results will set the stage to develop antimicrobial nanoparticle formulations for applications in chronic wound treatment.     

Thermally induced crystallization of mechanosynthesized chlorapatite–titania composite nanopowders

The thermally induced crystallization of mechanosynthesized chlorapatite–titania composite nanopowder was investigated. Firstly, the composite nanopowder was produced after 5 h of milling. The mechanosynthesized powder was annealed in the range 900–1300 °C for 1 h. The 5 h milled sample showed the crystallite size and lattice strain of about 23±1 nm and 0.0107±0.00054, respectively. During the subsequent annealing, crystallization of the milled sample at 900 °C and severe decomposition of nanocomposite at 1300 °C were detected. Accordingly, the fraction of crystalline phase reached a maximum around 96±4% at 900 °C and then declined to 80±4% at 1300 °C. The lattice strain decreased drastically to about 0.0002±0.00001 at 1300 °C, while the crystallite size increased significantly to around 277±14 nm. Based on the obtained data, the unit cell volume of CAp went down during the thermal treatment due to the ion exchange reaction between chlorapatite and titania. According to the electron microscopic observations, the morphological features of composite nanopowders were influenced strongly by the annealing temperature. The 5 h milled sample was composed of spheroidal particles with an average size of about 35 and 190 nm before and after annealing at 900 °C, respectively. At 1100 °C, the coalescence of nanospheres was dominant. Finally, a porous composite structure comprised of coarse grains with an average size of about 1 μm along with finer grains with a mean size of around 100 nm was formed at 1300 °C.     

A Novel Predictive Technique for Estimating the Hydrate Inhibition Effects of Single and Mixed Thermodynamic Inhibitors

A new correlation has been developed for predicting hydrate inhibition characteristics, as well as water freezing point depression of aqueous solutions containing salts and/or organic inhibitors. The correlation has been developed using hydrate inhibition/freezing point depression data at different concentrations of salt and/or organic inhibitor generated using a previously reported thermodynamic model. Independent data have been used in examining the reliability of this method. The predictions of this correlation are in acceptable agreement with the independent experimental data, demonstrating the reliability of this predictive technique for estimating the hydrate inhibition effects of salts and/or organic inhibitors.     

Eggshell nano-particle potential for methyl violet and mercury ion removal: Surface study and field application

A nano-scale sorbent was produced from eggshell wastes for sorption of Hg(II) and methyl violet (MV) from aqueous solutions and real wastewaters. The properties of the nano-particles were fully determined using SEM, DLS, FTIR, XRD, BET, TGA, AFM, EDAX, mapping, and TEM analyses. The adsorbent structure mainly contained carbonate and silica. The effects of influential parameters including temperature, contact time, initial contaminants concentration, sorbent dose, and initial pH on the removal efficiency were investigated. The maximum sorption efficiency of Hg(II) and MV occurred at pH of 6 and 9 and temperatures of 25 °C and 55 °C, respectively. Freundlich model could be interpreted the equilibrium data of the sorption process of both contaminants. The maximum sorption capacity of Hg(II) and MV using eggshell nano-particles was obtained as 116.27 mg/g and 123.45 mg/g, respectively. The dynamic behavior of the process was studied using two kinetic models. The sorption system performance was also examined and t_1/2 were determined as 4.34 min for Hg(II) and 4.97 min for MV. The sorption process of Hg(II) and MV was exothermic and endothermic, respectively. Effective sorption after seven cycles and successful treatment of landfill leachate and textile wastewater with eggshell nano-particles confirms its adequacy.     

A realistic multi-manned five-sided mixed-model assembly line balancing and scheduling problem with moving workers and limited workspace

The assembly line balancing problem can completely vary from one production line to the other. This paper deals with a realistic assembly line for the automotive industry inspired by Fiat Chrysler Automotive in North America and Parskhodro in Iran (both large-scale automotive companies). This problem includes some specific requirements that have not been studied in the literature. For example, the assembly line is five-sided, and workers can move along these sides. Due to the limited workspace, all the sides cannot work simultaneously at one station. First, a mixed integer linear programming model is proposed for the problem. Then, the model is improved to have a tighter linear relaxation. Moreover, an effective logic-based Benders’ decomposition algorithm is developed. After careful analysis of problem’s structure, three propositions are introduced. The master problem is well restricted by eight valid inequalities. Two different sub-problem types are defined to extract more information from the master problem’s solution. In this case, the algorithm adds effective cuts that reduce the solution space to the extent possible at each iteration. Thus, the number of iterations is significantly cut down. The performance of the model and algorithm, as well as improvement made on both, is evaluated.     

A performance model for analysis of heterogeneous multi-cluster systems

This paper addresses the problem of performance modeling for large-scale heterogeneous distributed systems with emphases on multi-cluster computing systems. Since the overall performance of distributed systems is often depends on the effectiveness of its communication network, the study of the interconnection networks for these systems is very important. Performance modeling is required to avoid poorly chosen components and architectures as well as discovering a serious shortfall during system testing just prior to deployment time. However, the multiplicity of components and associated complexity make performance analysis of distributed computing systems a challenging task. To this end, we present an analytical performance model for the interconnection networks of heterogeneous multi-cluster systems. The analysis is based on a parametric family of fat-trees, the m-port n-tree, and a deterministic routing algorithm, which is proposed in this paper. The model is validated through comprehensive simulation, which demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.     

Performance modeling and analysis of heterogeneous meta-computing systems interconnection networks

The overall performance of a distributed system often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems is very important, which is the focus of this paper. In particular, we address the problem of interconnection networks performance modeling for heterogeneous meta-computing systems. We consider the meta-computing system as a typical multi-cluster system. Since the heterogeneity is becoming common in such systems, we take into account network as well as cluster size heterogeneity to propose the model. To this end, we present an analytical network model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.