A thermodynamically consistent framework for non-isothermal modelling of local heat generation and electromotive force in SOFC single electrodes

Mathematical models for single electrode reversible heat and non-isothermal electromotive force (EMF) of a solid oxide fuel cell (SOFC) are developed. These models estimate the volumetric reversible heat generation and EMF of electrochemical reactions, within each electrode at local conditions of temperature and pressure, based on entropy change of half reactions. The resulting equations are thermodynamically consistent. They inherently obey the conservation of energy law as the electrochemical energy released added to the heat of reactions at each electrode equate the enthalpy change of the reacted species. The equations are implemented to model electrodes in a tubular micro- solid oxide fuel cell (TμSOFC). The thermodynamic consistency of the model is numerically confirmed as the enthalpy of the reactants equates the electric energy released by the cell plus the sum of electrode heats plus electrolyte Ohmic heat. The effect of thermal gradients on the cell's overall EMF is found to be negligible. The reversible and irreversible heat generation of each electrode are distinguished. Overall, the anode is found to be endothermic, and the cathode exothermic.     

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.     

Solid-phase electrochemical reduction of graphene oxide films in alkaline solution

Graphene oxide (GO) film was evaporated onto graphite and used as an electrode to produce electrochemically reduced graphene oxide (ERGO) films by electrochemical reduction in 6 M KOH solution through voltammetric cycling. Fourier transformed infrared and Raman spectroscopy confirmed the presence of ERGO. Electrochemical impedance spectroscopy characterization of ERGO and GO films in ferrocyanide/ferricyanide redox couple with 0.1 M KCl supporting electrolyte gave results that are in accordance with previous reports. Based on the EIS results, ERGO shows higher capacitance and lower charge transfer resistance compared to GO.     

Classification of Geothermal Energy Resources in Japan Applying Exergy Concept

Higher demand for energy consumption and importance of environmental issues has encouraged researchers and policy makers to consider renewable energies more seriously. Geothermal resources are a green energy source that can make a considerable contribution in some countries. Japan has the third ranking geothermal energy potential, and its geothermal electricity production is currently eighth in the world. Since the nature of geothermal resources dictates its method of utilization, it is important to categorize available resources. There is no consensus on classification of geothermal resources. Most scientists, from geologist to engineers, agree on the term temperature. However, temperature or enthalpy alone cannot describe the nature of fluids; they can have same temperature with different phases, such as saturated water or saturated steam. Using exergy for resource classification benefits their comparison, according to their ability to do work. In this paper, exergetic classification of geothermal resources was applied to 18 under‐operating geothermal power plants in Japan. Six geothermal fields have high exergy resources according to their SExI values in excess of 0.5. The remaining geothermal fields in Japan are classified in the medium resources zone. Classification results can be used by decision makers as a reference for future geothermal development. Copyright © 2012 John Wiley & Sons, Ltd.     

Dielectrophoretically assembled particles: feasibility for optofluidic systems

This work presents the dielectrophoretic manipulation of sub-micron particles suspended in water and the investigation of their optical responses using a microfluidic system. The particles are made of silica and have different diameters of 600, 450, and 250 nm. Experiments show a very interesting feature of the curved microelectrodes, in which the particles are pushed toward or away from the microchannel centerline depending on their levitation heights, which is further analyzed by numerical simulations. In doing so, applying an AC signal of 12 V_p–p and 5 MHz across the microelectrodes along with a flow rate of 1 μl/min within the microchannel leads to the formation of a tunable band of particles along the centerline. Experiments show that the 250 nm particles guide the longitudinal light along the microchannel due to their small scattering. This arrangement is employed to study the feasibility of developing an optofluidic system, which can be potentially used for the formation of particles-core/liquid-cladding optical waveguides.     

Phospholipids and oxophospholipids in atherosclerotic plaques at different stages of plaque development

We identified and quantified the hydroperoxides, hydroxides, epoxides, isoprostanes, and core aldehydes of the major phospholipids as the main components of the oxophospholipids (a total of 5–25 pmol/μmol phosphatidylcholine) in a comparative study of human atheroma from selected stages of lesion development. The developmental stages examined included fatty streak, fibrous plaque, necrotic core, and calcified tissue. The lipid analyses were performed by normal-phase HPLC with on-line electrospray MS using conventional total lipid extracts. There was great variability in the proportions of the various oxidation products and a lack of a general trend. Specifically, the early oxidation products (hydroperoxides and epoxides) of the glycerophosphocholines were found at the advanced stages of the plaques in nearly the same relative abundance as the more advanced oxidation products (core aldehydes and acids). The anticipated linear accumulation of the more stable oxidation products with progressive development of the atherosclerotic plaque was not apparent. It is therefore suggested that lipid infiltration and/or local peroxidation is a continuous process characterized by the formation and destruction of both early and advanced products of lipid oxidation at all times. The process of lipid deposition appears to have been subject to both enzymatic and chemical modification of the normal tissue lipids. Clearly, the appearance of new and disproportionate old lipid species excludes randomness in any accumulation of oxidized LDL lipids in atheroma.     

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.     

Aging fingerprint characterization of beer using electronic nose

In this work, attempts were made in order to characterize the change of aroma of alcoholic and non alcoholic beers during the aging process by use of a metal oxide semiconductor based electronic nose. The aged beer samples were statistically characterized in several classes. Linear techniques as principal component analysis (PCA) and Linear Discriminant Analaysis (LDA) were performed over the data that revealed non alcoholic beer classes are separated except a partial overlapping between zones corresponding to two specified classes of the aged beers. A clear discrimination was not found among the alcoholic beer classes showing the more stability of such type of beer compared with non alcoholic beer. In this research, to classify the classes, two types of artificial neural networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The classification success was found to be 90% and 100% for alcoholic and non alcoholic beers, respectively. Application of PNN showed the classification accuracy of 83% and 100%, respectively for the aged alcoholic and non alcoholic beer classes as well. Finally, this study showed the capability of the electronic nose system for the evaluation of the aroma fingerprint changes in beer during the aging process.