Improvement and stabilization white of bread flavour

Effect of adding the model system mixture (D,L-lysine · HCl, L-proline and D-glucose) on the sensory properties and volatile components of fresh bread and bread stored for 3 days were investigated. The results showed that the model system mixture improved the crust colour and flavour of fresh bread and kept the freshness, retarded the staling and improved the quality of the bread samples stored for 3 days. Analysis of the crust aroma of the bread samples showed that 2-acetyl pyrazine, 2-acetyl pyridine and 2-methyl-3-ethyl pyrazine, which may be responsible for bread crust aroma, were determined at high concentration in the bread sample containing 0.24% model system mixture. The concentration of pyrazine derivatives increased during storage whereas the carbonyl, pyrrol and furan derivatives decreased.     

Knowing Good From Bad: The Paradox of Neuroticism, Negative Affect, and Evaluative Processing.

There are pragmatic benefits to trait-consistent mood states, especially when people are evaluating new objects within the environment (M. Tamir, M. D. Robinson, & G. L. Clore, 2002). The present studies, involving both naturally occurring (Studies 1 and 2) and manipulated (Study 3) mood states, demonstrated such trait-consistent interactions within the context of neuroticism and negative mood states. Individuals high in neuroticism were faster to make evaluations when in a negative mood state like sadness. By contrast, individuals low in neuroticism were faster to make evaluations when in a neutral mood state. The present studies demonstrate that although negative mood states are hedonically unpleasant, they can be beneficial in some ways for individuals high in neuroticism. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system

The aim of this study is to introduce a comprehensive comparison of various energy management strategies of fuel cell/supercapacitor/battery storage systems. These strategies are utilized to manage the energy demand response of hybrid systems, in an optimal way, under highly fluctuating load condition. Two novel strategies based on salp swarm algorithm (SSA) and mine-blast optimization are proposed. The outcomes of these strategies are compared with commonly used strategies like fuzzy logic control, classical proportional integral control, the state machine, equivalent fuel consumption minimization, maximization, external energy maximization, and equivalent consumption minimization. Hydrogen fuel economy and overall efficiency are used for the comparison of these different strategies. Results demonstrate that the proposed SSA management strategy performed best compared with all other used strategies in terms of hydrogen fuel economy and overall efficiency. The minimum consumed hydrogen and maximum efficiency are found 19.4 gm and 85.61%, respectively.     

Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1‐0.5 mg/cm², the degree of sulphonation in sulfonated‐poly‐ether‐ether‐ketone that changes in the range of 20‐80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro‐fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well‐fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi‐object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m²) and 99.99 (%), respectively. Whereas, in the multi‐object optimization, the values of 61.787 (mW/m²) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.     

Integrated standalone hybrid solar PV,fuel cell and diesel generator power system for battery or supercapacitor storage systems in Khorfakkan,United Arab Emirates

Renewable energy resources play a very important rule these days to assist the conventional energy systems for doing its function in the UAE due to high greenhouse gas (GHG) emissions and energy demand. In this paper, the analysis and performance of integrated standalone hybrid solar PV, fuel cell and diesel generator power system with battery energy storage system (BESS) or supercapacitor energy storage system (SCESS) in Khorfakkan city, Sharjah were presented. HOMER Pro software was used to model and simulate the hybrid energy system (HES) based on the daily energy consumption for Khorfakkan city. The simulation results show that using SCESS as an energy storage system will help the performance of HES based on the Levelized cost of energy (LCOE) and greenhouse gas (GHG) emissions. The HES with SCESS has renewable fraction (68.1%) and 0.346 $/kWh LCOE. The HES meets the annual AC primary load of the city (13.6 GWh) with negligible electricity excess and with an unmet electrical load of 1.38%. The reduction in GHG emissions for HES with SCESS was 83.2%, equivalent to saving 814,428 gallons of diesel.     

Partial discharge pulse pattern recognition using Hidden Markov Models

An approach for the classification of cavity sizes based on their maximum charge transfer characteristics, applied voltage partial discharge pattern using Hidden Markov Models, is described. In these models, the partial discharge patterns for different cavity sizes are represented by a sequence of events rather than by the actual curves. In the training phase, each cavity size represents a unique class, which emits its own eigen sequence. Vector Quantization is deployed to assign labels for this particular sequence of events. A Hidden Markov Model is trained for each class, using a set of training patterns consisting of the labels produced by Vector Quantization. During testing, the sequence of events to be recognized is quantized and then matched against all the developed models. The best-matched model pinpoints the cavity size class. Experimental results demonstrate the remarkable capability of the proposed algorithm.