Recourse recovery of bioenergy from cellulosic material in a microbial fuel cell fed with giant reed-loaded wastewater

Ismail, Zainab Z.; Alwared, Abeer I.; Jaeel, Ali J.

doi:10.1080/17597269.2017.1409057

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…The main finding confirmed that both ANN and ANFIS models had a satisfactory correlation coefficient greater than 0.99. An ANN was trained based on an experimental dataset to predict the performance of MFC by Ismail et al 19 Different concentrations of giant reed and particle sizes were considered in the investigation. The output parameter of the ANN was defined as the MFC PD whereas three input parameters namely duration in days, the concentration of giant reed in wastewater and its particle sizes were used.…”

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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“…The behaviour of MFCs in general have been previously predicted by ML methods. Specifically, voltage outputs, Chemical Oxygen Demand removal rates, Coulombic efficiency and other characteristics of MFCs have been approximated by multilayer perceptron ANNs ( Tardast et al, 2012 ; Tardast et al, 2014 ; Jaeel et al, 2016 ; Ismail et al, 2017 ; Lesnik and Liu, 2017 ; Tsompanas et al, 2019 ; de Ramón-Fernández et al, 2020 ), multi-gene genetic programming ( Garg et al, 2014 ), adaptive neuro-fuzzy inference systems ( Esfandyari et al, 2016 ), nonparametric Gaussian process regression models ( He and Ma, 2016 ) and support vector regression forward and inverse model ( Wang et al, 2018 ). Despite the increasing popularity of modelling and optimizing MFC outputs with ML ( Ghasemi et al, 2020 ; Jadhav et al, 2020 ), implementing time series analysis is not that frequent.…”

Section: Previous Workmentioning

confidence: 99%

“…Despite the increasing popularity of modelling and optimizing MFC outputs with ML ( Ghasemi et al, 2020 ; Jadhav et al, 2020 ), implementing time series analysis is not that frequent. For instance, time parameters were used as inputs for neural networks in the study of MFCs ( Garg et al, 2014 ; Jaeel et al, 2016 ; Ismail et al, 2017 ); however, this methodology has some limitations. In specific, in time series analysis, a few past states of the system are more informative than the time past from the moment t = 0.…”

Section: Previous Workmentioning

confidence: 99%

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

et al. 2021

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The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.

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“…Moreover, the loading of common fuel for MFCs, such as domestic wastewater, with powdered giant reed, was studied and approximated by an ANN [22]. The network was trained with the data acquired by laboratory runs with different concentrations of giant reed and particle sizes.…”

Section: Previous Workmentioning

confidence: 99%

Artificial neural network simulating microbial fuel cells with different membrane materials and electrode configurations

Tsompanas

You

Wallis

et al. 2019

Journal of Power Sources

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Microbial fuel cells (MFCs) are gaining interest due to higher power production achieved by deep analysis of their characteristics and their effect on the overall efficiency. To date, investigations on MFC efficiency, can only be based on laboratory experiments or mathematical modelling. However, there is only a handful of rule-based mathematical modelling due to the difficulties imposed by the high sensitivity of the MFC system to environmental parameters and the highly complex bacterial consortia that dictate its behavior. Thus, an application of an artificial neural network (ANN) is proposed to simulate the polarisation of cylindrical MFCs with different materials as the separation membranes. ANNs are ideal candidates for investigating these systems, as there is no need for explicit knowledge of the detailed rules that govern the system.The ANN developed here is a feed-forward back-propagation network with a topology of 4-10-1 neurons that approximates the voltage of each MFC at a given state. Two different membrane materials with two different electrode configurations were assembled and utilized in laboratory experiments to produce the data set on which the ANN was trained upon. For the whole data set the correlation coefficient (R) between real values and outputs of the network was 0.99662.

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Recourse recovery of bioenergy from cellulosic material in a microbial fuel cell fed with giant reed-loaded wastewater

Cited by 13 publications

References 18 publications

Performance improvement of microbial fuel cell through artificial intelligence

Performance improvement of microbial fuel cell through artificial intelligence

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

Artificial neural network simulating microbial fuel cells with different membrane materials and electrode configurations

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