Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms

Nassef, Ahmed M.; Fathy, Ahmed; Sayed, Enas Taha; Abdelkareem, Mohammad Ali; Rezk, Hegazy; Tanveer, Waqas Hassan; Olabi, Abdul–Ghani

doi:10.1016/j.renene.2019.01.072

Cited by 110 publications

(31 citation statements)

References 26 publications

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“…The ANN has proved its efficiency in many engineering applications, especially in modeling problems [33]. Therefore, the data collected from the fruit drying experiments with and without acidic coating were used to build an ANN model for both the potato and apple.…”

Section: Ann Based Modelmentioning

confidence: 99%

The Effect of a New Coating on the Drying Performance of Fruit and Vegetables Products: Experimental Investigation and Artificial Neural Network Modeling

Rahman

Nassef

Al‐Dhaifallah

et al. 2020

Foods

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A study on mass transfer using new coating materials (namely alginic acid and polygalacturonic acid) during osmotic dehydration—and hence in a laboratory-scale convective dryer to evaluate drying performance—was carried out. Potato and apple samples were examined as model heat-sensitive products in this study. Results indicate that the coating material containing both alginic acid and polygalacturonic acid causes higher water loss of about 17% and 7.5% and lower solid gain of about 4% and 8%, respectively, compared to uncoated potato sample after a typical 90 min osmotic dehydration process. Investigation of drying performance using both coating materials showed a higher reduction in the moisture content of about 22% and 18%, respectively, compared with uncoated samples after the 3 h drying period. Comparisons between the two proposed coating materials were also carried out. Samples (potato) coated with alginic acid demonstrated better performance in terms of higher water loss (WL), lower solid gain (SG), and notable enhancement of drying performance of about 7.5%, 8%, and 8%, respectively, compared to polygalacturonic acid. Similar outcomes were observed using apple samples. Additionally, an accurate model of the drying process based on the experimental dataset was created using an artificial neural network (ANN). The obtained mean square errors (MSEs) for the predicted water loss and solid gain outputs of the potato model were 4.0948e−5 and 3.924e−6, respectively. However, these values for the same parameters were 3.164e−5 and 4.4915e−6 for the apple model. The coefficient of determination (r2) values for the two outputs of the potato model were found to be 0.99969 and 0.99895, respectively, while they were 0.99982 and 0.99913 for the apple model, which reinforces the modeling phase.

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Section: Ann Based Modelmentioning

confidence: 99%

The Effect of a New Coating on the Drying Performance of Fruit and Vegetables Products: Experimental Investigation and Artificial Neural Network Modeling

Rahman

Nassef

Al‐Dhaifallah

et al. 2020

Foods

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“…Therefore, the new velocity v(n + 1) and position p(n + 1) at a step (n + 1) are calculated for each particle from its previous velocity and position at a step (n). Hence, the updating rule is presented as in Equations (8) and (9). 41,42…”

Section: Global Mpp Tracking Based On Psomentioning

confidence: 99%

“…5,6 The most important advantages of these sources are cleanness, environmental protection, availability, and reliability. [7][8][9] Recently, many attempts were presented to enhance the efficacy of the photovoltaic (PV) technology depending on improving the semiconductor materials and improving the electronic interface circuit with the external load to increase the harvested energy. The efficiency of electric power generation based on the PV system (PVS) is low.…”

Section: Introductionmentioning

confidence: 99%

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

Rezk

2019

Int J Energy Res

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SUMMARY Solar energy and other renewables like geothermal, biomass, and wind energy can minimize the release of the CO2 and other harmful gases produced in case of fossil fuel. Low efficiency is the main drawback of the solar photovoltaic system specifically under partial shadowing condition (PSC). Commonly, with uniform solar radiation distribution, the power‐voltage graph has single maximum power point (MPP). The single MPP can be definitely extracted by any traditional tracker like perturb and observe as an example. However, during PSC, the situation is completely different since the power‐voltage curve has many MPPs (ie, multiple local points and single global point). The conventional MPP tracking methods cannot discriminate among local peaks and global peak; consequently, they can be easily trapped on the first local peak. Therefore, smart MPPTs based on modern optimization are required to track the global MPP. Most of MPPT tracking methods in the literature require both voltage and current sensors, and sometimes the control system needs an additional solar irradiance sensor and/or temperature sensor, which increase the system cost. In this paper, for the first time, a simple single‐sensor–based global MPP tracking method for partially shaded photovoltaic battery chargers is proposed. A deterministic particle swarm optimizer is utilized to extract the global MPP. Several patterns of PSC are considered to test and evaluate the proposed strategy. The obtained results confirm the efficacy of a single‐sensor–based global MPP tracking method to catch the global MPP accurately. Considering this research reduces the number of sensors, cost, and difficulty and consequently increases the power density of the MPP tracking methods under partial shadowing conditions.

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“…Experimental optimization of these operating parameters is not a simple task. However, this process requires long time, high cost and much efforts as well as it is limited by a small number of data samples too [11]. Mathematical modelling can overcome such difficulties by applying different modelling strategies to describe the system's performance and afterward the parameter optimization algorithm can be accomplished.…”

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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Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms

Cited by 110 publications

References 26 publications

The Effect of a New Coating on the Drying Performance of Fruit and Vegetables Products: Experimental Investigation and Artificial Neural Network Modeling

The Effect of a New Coating on the Drying Performance of Fruit and Vegetables Products: Experimental Investigation and Artificial Neural Network Modeling

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

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

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