An analytical-numerical approach for parameter determination of a five-parameter single-diode model of photovoltaic cells and modules

Parameter extraction of the five-parameter single-diode model of solar cells and modules from experimental data is a challenging problem. These parameters are evaluated from a set of nonlinear equations that cannot be solved analytically. On the other hand, a numerical solution of such equations needs a suitable initial guess to converge to a solution. This paper presents a new set of approximate analytical solutions for the parameters of a five-parameter single-diode model of photovoltaic (PV) cells and modules. The proposed solutions provide a good initial point which guarantees numerical analysis convergence. The proposed technique needs only a few data from the PV current-voltage characteristics, i.e. open circuit voltage V_oc, short circuit current I_sc and maximum power point current and voltage I_m; V_m making it a fast and low cost parameter determination technique. The accuracy of the presented theoretical I–V curves is verified by experimental data.     

Novel inorganic precursors [Co4.32Zn1.68(HCO2)18(C2H8N)6]/SiO2 and Co4.32Zn1.68(HCO2)18(C2H8N)6]/Al2O3 for Fischer–Tropsch synthesis

The silica- and alumina-supported Co–Zn catalysts were synthesized by thermal decomposition of new inorganic precursors [Co_4.32Zn_1.68(HCO₂)₁₈(C₂H₈N)₆]/SiO₂ or Al₂O₃. A novel coordination polymer formulated as [Co_4.32Zn_1.68(HCO₂)₁₈(C₂H₈N)₆] (1) was prepared using the solvothermal technique and characterized by elemental analysis, FT-infrared spectroscopy. Thermal stability of the complex 1 was investigated by thermogravimetric analysis and differential scanning calorimetry, and its structure was determined by single-crystal X-ray diffraction. Characterization of catalysts was carried out using powder X-ray diffraction (XRD), scanning electron microscopy (SEM), and BET specific surface area. The catalysts were evaluated for Fischer–Tropsch synthesis (FTS) in the temperature range 200–300 °C. The results revealed that the synthesized catalysts have higher selectivity to the desired products at 260 °C. The performance of the catalysts was compared to those of catalysts constructed via impregnation method and the fabricated catalysts show higher activity and selectivity than the reference catalysts.     

Cyclic scheduling of a robotic flexible cell with load lock and swap

In this paper, we study the problem of robotic cell scheduling with m machines with flexibility, load lock and swap assumptions. The robotic cell repetitively produces parts of identical types. We determine the cycle time of all 1-unit cycles in this type of robotic cell and present two new lower bounds for robot move cycles with load lock and swap, either there is flexibility or inflexibility. We also provide a new robot move cycle and prove that it dominates all classical robot move cycles considered in the existing literature of m-machine robotic cells.     

Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.     

A comparative study on optimised and non‐optimised axial flow,spherical reactors in naphtha reforming process

In this study, the operating conditions of an axial flow spherical reactor have been optimised using a reliable optimisation technique and the results are compared with the results of non‐optimised conditions. The dynamic behaviour of the reactor has been considered in the optimisation process and orthogonal collocation method has been used in order to solve the obtained equations from mathematical modelling of the process. The goal of this study is to maximise the aromatics and hydrogen production rate. Therefore, the objective function is the combination of two terms which include the production rate of the mentioned components. The catalyst distribution for each reactor, the inlet pressure of the system, Length per radius for each reactor, the naphtha feed molar flow rate and the hydrogen mole fraction in the recycle stream as well as the inlet temperature of each reactor have been optimised in this study. © 2011 Canadian Society for Chemical Engineering     

A local linear level set method for the binarization of degraded historical document images

Document image binarization is a difficult task, especially for complex document images. Nonuniform background, stains, and variation in the intensity of the printed characters are some examples of challenging document features. In this work, binarization is accomplished by taking advantage of local probabilistic models and of a flexible active contour scheme. More specifically, local linear models are used to estimate both the expected stroke and the background pixel intensities. This information is then used as the main driving force in the propagation of an active contour. In addition, a curvature-based force is used to control the viscosity of the contour and leads to more natural-looking results. The proposed implementation benefits from the level set framework, which is highly successful in other contexts, such as medical image segmentation and road network extraction from satellite images. The validity of the proposed approach is demonstrated on both recent and historical document images of various types and languages. In addition, this method was submitted to the Document Image Binarization Contest (DIBCO??09), at which it placed 3rd.     

Hybrid sonochemic urea-nitrate combustion preparation of CuO/ZnO/Al2O3 nanocatalyst used in fuel cell-grade hydrogen production from methanol: Effect of sonication and fuel/nitrate ratio

Fuel cell-grade hydrogen production has been studied via steam reforming of methanol (SRM) over a series of CuO/ZnO/Al₂O₃ nanocatalysts fabricated by the combustion method. The effect of sonication and urea/nitrate ratio on the characteristics and catalytic properties of the prepared catalysts has been investigated. The synthesized catalysts were characterized by x-ray diffraction (XRD), field emission scanning electron microscope (FESEM), Particle Size Distribution (PSD), energy dispersive x-ray (EDX), Brunauer-Emmett-Teller (BET) and FTIR analyses XRD patterns showed positive influence of urea/nitrate ratio on CuO and ZnO crystallite sizes. The ultrasonic mixing of primary gel compared with conventional mixing led to lower crystallite size. FESEM images showed that the sample mixed by sonication with a urea/nitrate ratio of 1 had more homogeneous morphology with narrow particle size distribution. EDX results proved the presence of all metals on the surface of the nanocatalysts and better consistence between the gel and surface composition of elements in samples prepared by sonication. Catalytic performance showed that sonication during the mixing of primary gel dramatically increased the methanol conversion. It was also proved that increasing the amount of urea led to lower catalytic activity. The ultrasound-treated nanocatalyst with urea/nitrate?=?1 was the best sample in terms of activity and selectivity. It was stable in the SRM for 1200?min without considerable change in methanol conversion and product selectivity.     

An exhaustive criterion for estimating quality of images in electrical impedance tomography with application to clinical imaging

This study proposes a versatile criterion for estimating quality of images in electrical impedance tomography. The point spread function (PSF) is calculated throughout the domain based on the scattering of energy as responses to a small anomaly spirally moved from the centre to the boundary. The proposed PSF is a measure of weighted spatial variance (WSV) of the conductivity over the whole domain. For each element, the weighting factor is a normalized multiplication of the area of that element by its square intensity. The WSV collectively incorporates all image attributes, i.e., spatial resolution, artifact, amplitude response, positioning error and shape deformation. The location of artifacts, which significantly influences reconstructed images in reality, is taken into account as well. The results illustrate that the proposed measure is more tolerant than existing criteria in evaluating performance of EIT systems in both theory and practice.