Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks

Lesnik, Keaton Larson; Liu, Hong

doi:10.1021/acs.est.7b01413

Cited by 77 publications

(41 citation statements)

References 56 publications

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“…Lesnik and Liu used ANN to model a set of 33 MFCs including eight separate substrates and three different wastewaters. 16 The mean percent predictions error values were 16.01 ± 4.35%, 1.77 ± 0.57% and 4.07 ± 1.06%, for PD, Coulombic efficiency and COD removal rate, respectively. Simulation of the polarization curves of MFC using ANN with considering different membrane materials and electrode configurations has been done by Tsompanas et al 17 Two different membrane materials with two different electrode configurations were considered during the experimental work for producing the dataset that employed to build the model of ANN.…”

Section: Introductionmentioning

confidence: 89%

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: 89%

Performance improvement of microbial fuel cell through artificial intelligence

et al. 2020

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“…Regardless of the feeding regime, the body of knowledge that compares the community and performance when fed different substrates does not have a strong consensus. Studies have shown that community structure, performance, and substrate composition may be related [3][4][5][6][7], while others have shown little correlation between these metrics [8][9][10]. Sampling differences may be playing a large role in these studies, as community structure can be dependent on where the community is sampled in the reactor.…”

Section: Introductionmentioning

confidence: 99%

Performance and Community Structure Dynamics of Microbial Electrolysis Cells Operated on Multiple Complex Feedstocks

Satinover

Rodríguez

Campa

et al. 2020

Preprint

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Background Microbial electrolysis is a promising technology for converting aqueous wastes into hydrogen. Substrate adaptability is an important feature, seldom documented in Microbial Electrolysis Cells (MECs). The correlation between substrate composition and community structure has not been well established. This study used a MEC capable of producing over 10 L/L-day of hydrogen from a switchgrass-derived bio-oil aqueous phase and investigated four additional substrates. The additional substrates included a red oak-derived bio-oil aqueous phase, a corn stover fermentation product, a mixture of phenol and acetate, and acetate alone. Results The MEC fed with the corn stover fermentation product resulted in the highest performance among the complex feedstocks, producing an average current density of 7.3 A/m2, although the acetate fed MEC outperformed complex substrates, producing 12 ± A/m2. 16S rRNA gene sequencing showed that community structure and community diversity were not predictive of performance, and replicate community structures diverged despite identical inoculum and enrichment procedure. The trends in each replicate, however, were indicative of the influence of the substrates. Geobacter was the most dominant genus across most of the samples tested, but its abundance did not correlate strongly to current density. High-performance liquid chromatography (HPLC) showed that acetate accumulated during open-circuit conditions when MECs were fed with complex feedstocks and was quickly degraded once closed-circuit conditions were applied. The largest net acetic acid removal rate occurred when MECs were fed with red oak bio-oil aqueous phase, consuming 2.93 ± 0.00 g/L-day. Principal component analysis found that MEC performance metrics such as current density, hydrogen productivity, and COD removal were closely correlated. Net acetic acid removal was also found to correlate with performance. However, no bacterial genus correlated to performance metrics, and the analysis suggested that less than 70% of the variance was accounted for by the two components. Conclusions This study demonstrates the robustness of microbial communities to adapt to a range of feedstocks and conditions without relying on specific species, delivering high hydrogen productivities, thus indicating functional adaptation vs. compositional requirement. MECs may,, play a central role in the 21st-century bioeconomy as factories producing a zero-emission fuel.

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“…In contrast to traditional mathematical models, scholars have done some research on the non-linear prediction model of the electrochemical process. Artificial Neural Networks (ANNs) do not require the modeling of a detailed mathematical formulation of a system and have been used to determine complex relationships between input and output data [15]. Daneshvar et al [16] established an ANN model for the decolorization process of dyeing wastewater by electroflocculation.…”

Section: Introductionmentioning

confidence: 99%

BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System

Mei

Yang

et al. 2019

IJERPH

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Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understanding of electrochemical treatment without increasing the cost. The backpropagation artificial neural network (BP–ANN) model was applied to predict chemical oxygen demand (COD) removal efficiency and total energy consumption (TEC). Current density, pH, supporting electrolyte concentration, and oxidation–reduction potential (ORP) were used as input parameters in the 2-chlorophenol synthetic wastewater model. Prediction accuracy was increased by using particle swarm optimization coupled with BP–ANN to optimize weight and threshold values. The particle swarm optimization BP–ANN (PSO–BP–ANN) for the efficient prediction of COD removal efficiency and TEC for testing data showed high correlation coefficient of 0.99 and 0.9944 and a mean square error of 0.0015526 and 0.0023456. The weight matrix analysis indicated that the correlation of the five input parameters was a current density of 18.85%, an initial pH 21.11%, an electrolyte concentration 19.69%, an oxidation time of 21.30%, and an ORP of 19.05%. The analysis of removal kinetics indicated that oxidation–reduction potential (ORP) is closely correlated with the chemical oxygen demand (COD) and total energy consumption (TEC) of the electro-oxidation degradation of 2-chlorophenol in wastewater.

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Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks

Cited by 77 publications

References 56 publications

Performance improvement of microbial fuel cell through artificial intelligence

Performance improvement of microbial fuel cell through artificial intelligence

Performance and Community Structure Dynamics of Microbial Electrolysis Cells Operated on Multiple Complex Feedstocks

BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System

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