Fuzzy modeling and parameters optimization for the enhancement of biodiesel production from waste frying oil over montmorillonite clay K-30

Inayat, Abrar; Nassef, Ahmed M.; Rezk, Hegazy; Sayed, Enas Taha; Abdelkareem, Mohammad Ali; Olabi, Abdul–Ghani

doi:10.1016/j.scitotenv.2019.02.321

Cited by 97 publications

(18 citation statements)

References 25 publications

(20 reference statements)

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“…The ANFIS as a modeling approach in conjunction with the modern optimization methods has been applied in several research applications. [22][23][24] These applications include; improving the biodiesel production from microalgae 22 and waste frying oil, 23 and maximization of the hydrogen and syngas productions from the palm kernel shell. 24 Numerous studies have been done on the essential parts of MFCs such as PEM, 25 biofilm and bacteria 26 and aeration rate 27 for improving the performance of MFCs.…”

Section: Introductionmentioning

confidence: 99%

Performance improvement of microbial fuel cell through artificial intelligence

et al. 2020

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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/cm 2 , 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/m 2) and 99.99 (%), respectively. Whereas, in the multi-object optimization, the values of 61.787 (mW/m 2) 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.

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Section: Introductionmentioning

confidence: 99%

Performance improvement of microbial fuel cell through artificial intelligence

et al. 2020

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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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“…Their results enhanced the production by 78% relative to the model based on the statistical modelling technique, RSM. The same technique has been also utilized by Inayat et al 52 to improve the biodiesel production from waste frying oil. Their work succeeded to estimate the optimal temperature, oil/methanol ratio, quantity of catalyst and reaction period, enhancing the biodiesel production by 15% over the earlier study.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial intelligence to maximize methane production from waste paper

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This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic-based modelling and modern optimization. Firstly, a robust Adaptive Network-based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions.

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Fuzzy modeling and parameters optimization for the enhancement of biodiesel production from waste frying oil over montmorillonite clay K-30

Cited by 97 publications

References 25 publications

Performance improvement of microbial fuel cell through artificial intelligence

Performance improvement of microbial fuel cell through artificial intelligence

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

Application of artificial intelligence to maximize methane production from waste paper

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