Modeling and Parametric Study of a Single Solid Oxide Fuel Cell by Finite Element Method

A 2D isothermal axisymmetric model of an anode‐supported solid oxide fuel cell has been developed. The model, which is based on finite element approach, comprises electronic and ionic charge balance, Butler–Volmer charge transfer kinetic, flow distribution and gas phase mass balance in both channels and porous electrodes. The model has been validated using available experimental data coming from a single anode‐supported cell comprising Ni–YSZ/YSZ/LSM–YSZ as anode, electrolyte and cathode, respectively. Hydrogen and steam were used as fuel inlet and air as an oxidant. The validation has been carried out at 1 atm, 1,500 ml min^–1 air flow rate and three different operating conditions of temperature and fuel flow rate: 1,073 K and 400 ml min^–1, 1,073 K and 500 ml min^–1, and 1,003 K and 400 ml min^–1. The polarization and power density versus current density curves show a good agreement with the experimental data. A parametric analysis has been carried out to highlight which parameters have main effect on the overall cell performance as measured by polarization curve, especially focusing on the influence of two geometrical characteristics, temperature and some effective material properties.     

Photooxidative degradation of Acid Red 27 in a tubular continuous-flow photoreactor: influence of operational parameters and mineralization products

The decolorization and mineralization of Acid Red 27 (AR27), an anionic monoazo dye of acid class, was investigated using UV radiation in the presence of H2O2 in a tubular continuous-flow photoreactor as a function of oxidant concentration, reactor length, flow rate and light intensity. The removal efficiency of AR27 depends on the operational parameters and increases as the initial concentration of H2O2 and light intensity are increased but it decreases when the flow rate is increased. The AR27 degradation was followed through HPLC, UV-vis and COD analyses. The results of these analyses showed that the final outlet stream from the photoreactor was completely mineralized. The UV/H2O2 process was also able to mineralize nitrogen and sulfur heteroatoms into NH4+, NO3-, NO2- and SO4(2-) ions, respectively. The nitrogen of azo group was transformed predominantly to NH4+ ions. Decreasing the flow rate results in the reduction of COD and promotion of SO4(2-) at the final outlet stream of the photoreactor.     

Decolorization of basic dye solutions by electrocoagulation: an investigation of the effect of operational parameters

Electrocoagulation (EC) is one of the most effective techniques to remove color and organic pollutants from wastewater, which reduces the sludge generation. In this paper, electrocoagulation has been used for the removal of color from solutions containing C. I. Basic Red 46 (BR46) and C. I. Basic Blue 3 (BB3). These dyes are used in the wool and blanket factories for fiber dyeing. The effect of operational parameters such as current density, initial pH of the solution, time of electrolysis, initial dye concentration and solution conductivity were studied in an attempt to reach higher removal efficiency. The findings in this study shows that an increase in the current density up to 60-80 A m(-2) enhanced the color removal efficiency, the electrolysis time was 5 min and the range of pH was determined between 5.5 and 8.5 for two mentioned dye solutions. It was found that for, the initial concentration of dye in solutions should not be higher than 80 mg l(-1) in order to achieve a high color removal percentage. The optimum conductivity was found to be 8 mS cm(-1), which was adjusted using proper amount of NaCl with the dye concentration of 50 mg l(-1). Electrical energy consumption in the above conditions for the decolorization of the dye solutions containing BR46 and BB3 were 4.70 kWh(kgdye removed)(-1) and 7.57 kWh(kgdye removed)(-1), respectively. Also, during the EC process under the optimized conditions, the COD decreased by more than 75% and 99% in dye solutions containing BB3 and BR46, respectively.     

Deep learning and optimization algorithms for automatic breast cancer detection

Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-of-the-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy.     

Photocatalytic degradation of C.I. Acid Red 27 by immobilized ZnO on glass plates in continuous-mode

The photocatalytic degradation of C.I. Acid Red 27 (AR27), an anionic monoazo dye of acid class, in aqueous solutions was investigated with immobilized ZnO catalyst on glass plates in a continuous-mode. In the slurry ZnO system the separation and recycling of the photocatalyst is practically difficult. Thus, ZnO was immobilized on solid supports to solve this problem. The removal percent increases with increasing the photoreactor volume and light intensity but it decreases when the flow rate is increased. With decreasing flow rate from 43 to 15mlmin(-1), the complete decolorization and degradation was obtained at around 748 and 1080cm(3) from photoreactor volume. The increase in the light intensity from 21.4 to 58.5Wm(-2) increases the decolorization from 23 to 57.6% and degradation from 17.5 to 37.8% for 374cm(3) of photoreactor volume. NH(4)(+), NO(3)(-), NO(2)(-) and SO(4)(2-) ions were analyzed as mineralization products of nitrogen and sulfur heteroatoms. Results showed that final concentration of SO(4)(2-) ions and N-containing mineralization products were less than the finally expected stoichiometric values. The positive slope of production of NH(4)(+), NO(3)(-) and NO(2)(-) shows that these compounds are initial products resulting directly from the initial attack on the nitrogen-to-nitrogen double bond (-NN-) of the azo dye.     

A literature review on MHE selection problem: levels,contexts, and approaches

This paper presents a review on selection problem of material handling equipment (MHE) and general equipment used in industry area. The issue on MHE is widely paid attention since MHE has contribution on material, good and product accomplishment. Few methods and softwares have been proposed and developed to select the most appropriate MHE for a complex selection problem. Today’s high diverisity of MHE categories and types influence the generation of many possible choices which leads to the complexity. In this paper, a further discussion in terms of MHE and equipment including three major points namely level of selection, the context of selection problem and the approaches are served to highlight the complex MHE selection according to the number of possible choices provided, to analyse the consideration for the problem context, and to reveal the superior method for complex MHE selection. Forty-two papers collected from the past study are presented asscociating each point of the discussion.     

Evaluation of 1-dimensional nanomaterials release during electrospinning and thermogravimetric analysis

The growing research interests with engineered nanomaterials in academic laboratories and manufacturing facilities pose potential safety risks to students and workers. New nanoparticle substances, compositions, and processing approaches are developed regularly, creating new health risks which may not have been addressed previously. Accordingly, the Institute of Occupational Medicine conducted field studies at Texas A&M University (TAMU) to characterize possible particle emissions during processing and fabrication of carbon nanotubes, copper nanowires, and polymeric fibers. The nature of the monitoring work carried out at TAMU was to investigate the potential release of 1D nanomaterials to air from activities associated with synthesis, handling, thermal gravimetric analysis, and electrospinning processes, and evaluate the effectiveness of the utilized control measures. The potential nanoparticle release to air from each activity was investigated using a combination of particle detection instrumentations, coupled with standard filter-based sampling techniques. The analyses indicated that a measurable quantity of free carbon nanosphere aggregates was detected during these activities; however, no free MWCNTs or nanowires were detected. Scanning electron microscopy identified the presence of carbon nanospheres aggregates on the filters. While the control measures used at TAMU are effective in containing the nanomaterial release during processing, poor handling and occupational hygiene practices can increase the risk of employee exposure to the nanomaterials.     

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.     

Fusion of multimodal medical images using nonsubsampled shearlet transform and particle swarm optimization

Multidimensional Systems and Signal Processing - Medical imaging has been an indispensable tool in modern medicine in last decades. Various types of imaging systems provide structural and...     

An Application of the Fuzzy ELECTRE Method for Academic Staff Selection

There are various methods regarding staff selection in different fields. Thanks to the increasing improvements in the field of education, universities around the world tend to demand high ‐quality and professional academic staff. Staff selection is a multi‐criteria decision‐making processes, and of strategic importance for most universities. This study deals with actual application of academic of staff selection using the opinion of experts to be applied into a model of group decision ‐ making called the Fuzzy ELECTRE (Elimination Et Choix Traduisant la REaite) method. There are ten qualitative criteria for selecting the best candidate amongst five prospective applications. © 2012 Wiley Periodicals, Inc.