Application of cold filamentary microplasma pretreatment assisted by thermionic emission for potato drying

Cold filamentary microplasma (CFM) pretreatment assisted by thermionic emission (TE) is a promising, cold drying technology for agricultural products. Such novel pretreatment method based on local electroporation process in the air gap. Brief theory and treatment mechanism are explained. A fractional factorial design 3³ was used to determine the effect of CFM treatment assisted by TE parameters such as pulse frequency (F = 40, 80 and 120 Hz), pretreatment duration (D = 60, 120 and 180 s) and temperature of thermionic source (T = 700, 850 and 1000 °C) on drying time and drying rate of potato samples. Maximal CFM pretreatment effect was observed at frequency of 80 Hz, treatment duration of 120 s and temperature of thermionic source of 1000 °C, and the corresponding drying rate and drying time were 35·10⁻⁵ (kg/kg s⁻¹) and 130 min. The use of CFM treatment had minor effects on the main quality characteristics (total sugar and starch content) of potato samples. Fourier-transformed infrared spectroscopy confirmed the absence of any modifications of functional group composition of potato samples after CFM pretreatment. Results indicate that cold pulsed filamentary microplasma can enhance the drying rate and decrease drying time without changes in quality characteristics.Practical applicationsCold filamentary microplasma pretreatment assisted by thermionic emission can improve the efficiency of potato drying. Furthermore, it has potential application for reducing energy consumption in drying. CFM pretreatment could be potentially applied for agricultural products drying.     

Iron mediated cathodic electrosynthesis of hausmannite nanoparticles

A novel synthetic route has been proposed to prepare hausmannite nanoparticles. The synthetic route comprises an iron mediated constant current cathodic electrodeposition of manganite and heat treatment of the latter to obtain hausmannite. The obtained nanostructures have been characterized using X-ray Diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDX) and Fourier transform Infrared Spectrometry (FTIR). The role of iron in the formation of manganite precursor has been studied by cyclic voltammetry (CV) and differential thermal analysis (DTA). A formation mechanism based on iron mediated formation of Mn³⁺ and subsequent cathodic reduction of the disproportionated products has been proposed accordingly. The prepared nanoparticles exhibited specific capacitance of 143 F g⁻¹ in 0.5 M Na₂SO₄ solution. The retained specific capacity was 87% after 2000 cycles.     

An efficient video summarization for surveillance system using normalized k-means and quick sort method

There has been a vast augmentation in quantity of Video Content (VC) generated amid the last some years. The Video Summarization (VS) approach is introduced for managing the VC. Prevailing VS techniques have endeavored to render the VS but the systems have Execution Time (ET) as well as condensing the video's content in domain specific manner. To triumph over such disadvantages, this paper proposed efficient VS for surveillance system using normalized k-means along with quick sort method. The proposed technique comprises eight stages, like split video into frames, pre-sampling, provide ID number, feature extraction, Feature Selection (FS), clustering, extract frames, video summary. Initially, the video frames are pre-sampled utilizing the proposed Three Step Cross Searching Algorithm (TSCS) technique. Then, give the ID number for every frame. Next, the features are extracted as of the frames. Then, the necessary features are selected using Entropy based Spider Monkey Algorithms (ESMA). In next stage, the features are grouped using Normalized K-Means (N-Kmeans) algorithm for identifying best candidate frames. Then select the minimum distance value based cluster set is the Key Frame (KF) selection. At last, the video is orderly summarized using quick sort method. Finally, in experimental evaluation the proposed work is compared with the prevailing methods. The proposed VS gave better outcome than the existing approaches.     

Bond graph modeling,design and experimental validation of a photovoltaic/fuel cell/ electrolyzer/battery hybrid power system

This work presents a complete bond graph modeling of a hybrid photovoltaic-fuel cell-electrolyzer-battery system. These are multi-physics models that will take into account the influence of temperature on the electrochemical parameters. A bond graph modeling of the electrical dynamics of each source will be introduced. The bond graph models were developed to highlight the multi-physics aspect describing the interaction between hydraulic, thermal, electrochemical, thermodynamic, and electrical fields. This will involve using the most generic modeling approach possible for managing the energy flows of the system while taking into account the viability of the system. Another point treated in this work is to propose. In this work, a new strategy for the power flow management of the studied system has been proposed. This strategy aims to improve the overall efficiency of the studied system by optimizing the decisions made when starting and stopping the fuel cell and the electrolyzer. It was verified that the simulation results of the proposed system, when compared to simulation results presented in the literature, that the hydrogen demand is increased by an average of 8%. The developed management algorithm allows reducing the fuel cell degradation by 87% and the electrolyzer degradation by 65%. As for the operating time of the electrolyzer, an increment of 65% was achieved, thus improving the quality of the produced hydrogen. The Fuel Cell's running time has been decreased by 59%. With the ambition to validate the models proposed and the associated commands, the development of this study gave rise to the creation of an experimental platform. Using this high-performance experimental platform, experimental tests were carried out and the results obtained are compared with those obtained by simulation under the same metrological conditions.     

Comparative evaluation of the mesophilic and thermophilic biohydrogen production at optimized conditions using tequila vinasses as substrate

The objective of this work was to comparatively evaluate the production of biohydrogen (bio-H₂) from tequila vinasses at optimized mesophilic and thermophilic conditions and to elucidate the main metabolic routes involved. Optimal temperatures of 35 °C and 55 °C, and pH of 5.5 maximized the bio-H₂ production rates, 25.5 ± 0.01 NmL h⁻¹ and 169.9 ± 8.9 NmL h⁻¹ in the mesophilic and thermophilic regimens, respectively. During the operation of anaerobic sequencing batch reactors, the thermophilic process allowed a volumetric bio-H₂ production rate of 519 ± 13 NmL-H₂ L⁻¹ d⁻¹ equivalent to 750 ± 19 NmL-H₂ L_vinasse⁻¹, while the mesophilic one 448 ± 23 NmL-H₂ L⁻¹ d⁻¹ and 647 ± 33 NmL-H₂ L_vinasse⁻¹, respectively. Furthermore, the gas produced under thermophilic conditions showed high hydrogen content (86.5%). Finally, formate degradation and glucose fermentation to acetic and butyric acids were the main metabolic routes involved in bio-H₂ production under thermophilic conditions, while at mesophilic conditions, the lactate and formate degradation pathways governed.     

Voltage reference architectures for low-supply-voltage low-power applications

This paper presents a voltage reference generator architecture and two different realizations of it that have been fabricated within a standard 0.18 μm CMOS technology. The architecture takes the advantage of utilizing a sampled-data amplifier (SDA) to optimize the power consumption. The circuits achieve output voltages on the order of 190 mV with temperature coefficients of 43 ppm/°C and 52.5 ppm/°C over the temperature range of 0 to 120°C without any trimming with a 0.8 V single supply. The power consumptions of the circuits are less then 500 nW while occupying an area of 0.2 mm² and 0.08 mm², respectively.     

A design of a robust discrete-time controller

In this paper, we proposed a robust discrete-time controller. This control system, which is derived from the idea of the normalized plant, does not include plant parameters. Thus, we obtain a control system independent of plant parameters and that has the same structure as a conventional optimal servo control system. Simulation and experimental results show that the proposed method is fairly robust to plant parameter variations and external disturbances.     

Convergence analysis of the alternating RGLS algorithm for the identification of the reduced complexity Volterra model

In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS).