Identifying optimal operating conditions of solar-driven silica gel based adsorption desalination cooling system via modern optimization

Rezk, Hegazy; Alsaman, Ahmed S.; Al‐Dhaifallah, Mujahed; Askalany, Ahmed A.; Abdelkareem, Mohammad Ali; Nassef, Ahmed M.

doi:10.1016/j.solener.2019.02.024

Cited by 71 publications

(21 citation statements)

References 54 publications

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“…During the optimization process, the decision variables are represented by three different operating parameters: temperature and the volume fractions of both Al 2 O 3 and TiO 2 to enhance the performance of nanofluid mixture which is utilized as a cost function. Due to the stochastic behavior of the swarm optimizers, the optimization results cannot be trusted unless many trials have been done [6], [7]. Therefore, the optimization process was executed for 100 times.…”

Section: Datamentioning

confidence: 99%

Dataset on fuzzy logic based-modelling and optimization of thermophysical properties of nanofluid mixture

et al. 2019

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This article presents the dataset generated during the process of enhancing the thermophysical properties of nanofluid mixture through fuzzy logic based-modelling and particle swarm optimization (PSO) algorithm. The details of fuzzy model and optimization phases were discussed in our work entitled “Fuzzy modeling and optimization for experimental thermophysical properties of water and ethylene glycol mixture for Al2O3 and TiO2 based nanofluids” (Said et al., 2019). In (Said et al., 2019), the detail of the numerical data has not been clearly presented. However, in this article the inputs’ data values for the density, viscosity, and thermal conductivity, used for training and testing of the fuzzy model, have been mentioned which is very essential if the model has to be rebuilt again. Furthermore, the resulting data variation of the cost function for the 100 runs during the optimization process that had not been presented in (Said et al., 2019) is presented in this work. These data sets can be used as references to analyze the data resulting from any other optimization technique. The datasets are provided in the supplementary materials in Tables 1–4

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Section: Datamentioning

confidence: 99%

Dataset on fuzzy logic based-modelling and optimization of thermophysical properties of nanofluid mixture

et al. 2019

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“…Current energy relies on fossil fuels, which not only are limited in resources, but also result in high quantities of pollution and health risks [1][2][3]. The environmental impact of fossil fuels can be minimized through increasing the efficiency of current technologies [4,5] and/or relying on renewable energy sources [6], such as biomass energy [7][8][9], wind energy [10,11], solar thermal energy [12,13], solar PV [14][15][16], ocean energy [17], and geothermal energy [9]. Fuel cells are emerging energy conversion devices that are silent, have a high theoretical efficiency [18][19][20], and exhibited high performance when installed in several applications [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Carbon-Cloth Anode Electroplated with Iron Nanostructure for Microbial Fuel Cell Operated with Real Wastewater

et al. 2020

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Microbial fuel cell (MFC) is an emerging method for extracting energy from wastewater. The power generated from such systems is low due to the sluggish electron transfer from the inside of the biocatalyst to the anode surface. One strategy for enhancing the electron transfer rate is anode modification. In this study, iron nanostructure was synthesized on a carbon cloth (CC) via a simple electroplating technique, and later investigated as a bio-anode in an MFC operated with real wastewater. The performance of an MFC with a nano-layer of iron was compared to that using bare CC. The results demonstrated that the open-circuit voltage increased from 600 mV in the case of bare CC to 800 mV in the case of the iron modified CC, showing a 33% increase in OCV. This increase in OCV can be credited to the decrease in the anode potential from 0.16 V vs. Ag/AgCl in the case of bare CC, to −0.01 V vs. Ag/AgCl in the case of the modified CC. The power output in the case of the modified electrode was 80 mW/m2—two times that of the MFC using the bare CC. Furthermore, the steady-state current in the case of the iron modified carbon cloth was two times that of the bare CC electrode. The improved performance was correlated to the enhanced electron transfer between the microorganisms and the iron-plated surface, along with the increase of the anode surface- as confirmed from the electrochemical impedance spectroscopy and the surface morphology, respectively.

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“…Compared with Response Surface Methodology, the performance is increased by 78.7 %. The best-operating conditions of solar-driven silica gel-based adsorption desalination cooling system are determined by radial movement optimizer [14]. These parameters include; cycle time, hot water inlet temperature, cooling water inlet temperature, and flow rate.…”

Section: Introductionmentioning

confidence: 99%

ANFIS-Based Modelling and Optimal Operating Parameter Determination to Enhance Cocoa Beans Drying-Rate

et al. 2020

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This work aims to maximize the drying-rate of the cocoa beans by controlling the values of temperature, pressure, and moisture contents. The drying-rate is maximized by defining the optimal operating conditions. Using the experimental data, obtained from the drying-rate process, a robust model that describes the drying-rate of the cocoa beans is generated using fuzzy logic approach. Then, the optimal operating conditions of the system are determined using Particle Swarm Optimization (PSO) algorithm. Three different operating parameters that influence the drying rate of the cocoa beans are assessed. These parameters are the temperature (• C), pressure (in.Hg), and moisture contents. Accordingly, during the optimization process, these parameters are used as the decision variables for the PSO optimizer in order to maximize the drying-rate which is used as a cost function. The resulting plots demonstrated a well-fitting between the output of the fuzzy model and the experimental data. The mean square errors of the resulting predictions are found 0.000297 and 0.00012 for testing and whole datasets, respectively. Based on the built model, the optimization process achieved a significant increase in the drying-rate to a value of 2.12 relative to 1.315 which was obtained experimentally. This proved that the fuzzy modelling with PSO attained an increase of 61.14% without changing the system's design or the materials used. Indeed, the originality of this research work promotes the proposed model to be a valuable tool for the design of future dryers. INDEX TERMS Fuzzy modelling, optimization, vacuum drying, cocoa beans.

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Identifying optimal operating conditions of solar-driven silica gel based adsorption desalination cooling system via modern optimization

Cited by 71 publications

References 54 publications

Dataset on fuzzy logic based-modelling and optimization of thermophysical properties of nanofluid mixture

Dataset on fuzzy logic based-modelling and optimization of thermophysical properties of nanofluid mixture

A Carbon-Cloth Anode Electroplated with Iron Nanostructure for Microbial Fuel Cell Operated with Real Wastewater

ANFIS-Based Modelling and Optimal Operating Parameter Determination to Enhance Cocoa Beans Drying-Rate

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