# Adaptive Neuro-Fuzzy Inference System Modeling and Optimization of Microbial Fuel Cells for Wastewater Treatment

^{1}

^{2}

^{3}

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## Abstract

**:**

## 1. Introduction

## 2. Microbial Fuel Cells

_{3}. Wastewater containing Cr(VI) must be treated to lessen its adverse effects on ecosystems and human health. Chemical precipitation, adsorption, ion exchange, membrane filtration, and biological therapy are some of the treatment techniques that can be used. Each approach has pros and cons, and the choice is made based on the baseline Cr(VI) concentration, the required level of treatment efficacy, cost, and infrastructure accessibility. Cr(VI) concentrations can be effectively brought down to safe levels by effective treatment techniques, allowing for regulatory compliance and reducing environmental contamination. To ensure the preservation of the environment and human health, applying effective treatment technologies and adopting sound management practices for Cr(VI) is essential. Additionally, continuous monitoring and adherence to environmental standards are crucial to stopping the discharge of Cr(VI) into ecosystems and water bodies.

^{2}. Meanwhile, the cylindrical cathode was made of conductive carbon black combined with 10.24% wt polyvinyl alcohol (PVA). External resistors of 12 mm in length were used to link the anode and cathode. More details can be found in [32]. The number of data points is 19. Three input parameters are used as follows. The lower and upper limits for Cu(II)/Cr(VI) ratio are 0.33 and 1.672, respectively. The lower and upper percentages of the substrate concentration are 0.244 and 1.756. The minimum and maximum external resistance values are 244 Ω and 1000 Ω, respectively. Under these conditions, the power density (PD) of the MFC system ranged from 0.45 mW/ m

^{2}to 36.76 mW/m

^{2}, and the Cr(VI) removal (RE) ranged from 30% to 75%, suggesting that the PD and Cr(VI) RE of the MFC system were affected by the Cu(II)/Cr(VI) ratio, substrate concentration, and external resistance. A modeling approach may successfully handle small amounts of nonlinear data. ANFIS combines the advantages of fuzzy logic and neural networks to create a hybrid model capable of capturing nonlinear relationships; therefore, it is used in the current study.

## 3. ANFIS Model of MFCs

_{1}and y is B

_{1}then f

_{1}= g

_{1}(x, y)

_{2}and y is B

_{2}then f

_{2}= g

_{2}(x, y)

_{1}and B

_{1}are the MFs of the two inputs x and y.

## 4. Parameter Identification by an Artificial Ecosystem Optimizer

_{1}and r are random factors, and x

_{rand}is a random position produced in the search space. The consumption operator can be modeled as

## 5. Results and Discussion

#### 5.1. Modeling Phase

^{−6}and 1.0598, respectively. For training and testing, the coefficients of determination are 1.0 and 0.9864, respectively. The coefficient of determination is enhanced from 0.992 (by ANOVA) to 0.9981 (by ANFIS), and the RMSE is decreased from 16.486 (by ANOVA) to 0.4863 (by ANFIS). For the training and testing data sets, the RMSE values for the ANFIS model of Cr(VI) removal efficiency are 2.63 × 10

^{−5}and 2.1275, respectively. For training and testing, the coefficients of determination are 1.0 and 0.9991, respectively. The coefficient of determination is enhanced from 0.951 (by ANOVA) to 0.9963 (by ANFIS), and the RMSE is decreased from 22.60 (by ANOVA) to 0.9761 (by ANFIS). This shows that the fuzzy modeling phase was effective. Figure 3 depicts the 3-input, single-output fuzzy model architecture, and Figure 4 depicts the general contours of the Gaussian form of MFs.

#### 5.2. Optimization Phase

^{2}to 38.96 mW/m

^{2}(by 19.14%) compared to measured data. In addition, boosting the Cr(VI) removal efficiency from 71% to 81.75% (by 15.14%) compared to measured data. Under this condition, the optimal values are 1.672, 1.756 (g/L), and 1404.8 Ω, respectively, for the Cu(II)/Cr(VI) ratio, substrate concentration, and external resistance. Figure 8 shows the particle convergence of objective function, normalized Cu(II)/Cr(VI) ratio, normalized substrate concentration, and normalized external resistance. The figure demonstrated that all particles converged to the optimal value after 35 iterations.

## 6. Conclusions

^{−5}and 2.1275, respectively. For training and testing, the coefficients of determination are 1.0 and 0.9991, respectively. The coefficient of determination is enhanced from 0.951 (by ANOVA) to 0.9963 (by ANFIS), and the RMSE is decreased from 22.60 (by ANOVA) to 0.9761 (by ANFIS). This shows that the fuzzy modeling phase was effective. Finally, the integration between ANFIS and AEO increased the power density from 32.7 mW/m

^{2}to 38.96 mW/m

^{2}, by 19.14%, compared to measured data. In addition, it boosted the Cr(VI) removal efficiency from 71% to 81.75%, by 15.14%, compared to measured data. Under this condition, the optimal values are 1.672, 1.756 (g/L), and 1404.8 Ω, respectively, for the Cu(II)/Cr(VI) ratio, substrate concentration, and external resistance. The obtained results are not verified experimentally; therefore, they could be used as a basis for further investigations using more parameters than those investigated in the current research.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Lazaro, L.L.B.; Bellezoni, R.A.; Puppim de Oliveira, J.A.; Jacobi, P.R.; Giatti, L.L. Ten Years of Research on the Water-Energy-Food Nexus: An Analysis of Topics Evolution. Front. Water
**2022**, 4, 859891. [Google Scholar] [CrossRef] - Zhang, C.; Chen, X.; Li, Y.; Ding, W.; Fu, G. Water-energy-food nexus: Concepts, questions and methodologies. J. Clean. Prod.
**2018**, 195, 625–639. [Google Scholar] [CrossRef] - Abulibdeh, A.; Zaidan, E. Managing the water-energy-food nexus on an integrated geographical scale. Environ. Dev.
**2020**, 33, 100498. [Google Scholar] [CrossRef] - James Rubinsin, N.; Daud, W.R.W.; Kamarudin, S.K.; Masdar, M.S.; Rosli, M.I.; Samsatli, S.; Tapia, J.F.D.; Wan Ab Karim Ghani, W.A.; Hasan, A.; Lim, K.L. Modelling and optimisation of oil palm biomass value chains and the environment–food–energy–water nexus in peninsular Malaysia. Biomass Bioenergy
**2021**, 144, 105912. [Google Scholar] [CrossRef] - Jin, Y.; Behrens, P.; Tukker, A.; Scherer, L. Water use of electricity technologies: A global meta-analysis. Renew. Sustain. Energy Rev.
**2019**, 115, 109391. [Google Scholar] [CrossRef] - Bazzana, D.; Comincioli, N.; El Khoury, C.; Nardi, F.; Vergalli, S. WEF Nexus Policy Review of Four Mediterranean Countries. Land
**2023**, 12, 473. [Google Scholar] [CrossRef] - Albrecht, T.R.; Crootof, A.; Scott, C.A. The Water-Energy-Food Nexus: A systematic review of methods for nexus assessment. Environ. Res. Lett.
**2018**, 13, 043002. [Google Scholar] [CrossRef] - Longo, S.; d’Antoni, B.M.; Bongards, M.; Chaparro, A.; Cronrath, A.; Fatone, F.; Lema, J.M.; Mauricio-Iglesias, M.; Soares, A.; Hospido, A. Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement. Appl. Energy
**2016**, 179, 1251–1268. [Google Scholar] [CrossRef] - Yu, Y.; Zou, Z.; Wang, S. Statistical regression modeling for energy consumption in wastewater treatment. J. Environ. Sci.
**2019**, 75, 201–208. [Google Scholar] [CrossRef] - Ibrahim, N.; Kamarudin, S.K.; Minggu, L.J. Production of electricity from ethanol and ammonium based wastewater via photo-electrochemical process. Int. J. Hydrogen Energy
**2017**, 42, 9051–9062. [Google Scholar] [CrossRef] - Kollmann, R.; Neugebauer, G.; Kretschmer, F.; Truger, B.; Kindermann, H.; Stoeglehner, G.; Ertl, T.; Narodoslawsky, M. Renewable energy from wastewater—Practical aspects of integrating a wastewater treatment plant into local energy supply concepts. J. Clean. Prod.
**2017**, 155, 119–129. [Google Scholar] [CrossRef] - Rusli, S.F.N.; Daud, S.M.; Abu Bakar, M.H.; Loh, K.S.; Masdar, M.S. Biotic Cathode of Graphite Fibre Brush for Improved Application in Microbial Fuel Cells. Molecules
**2022**, 27, 1045. [Google Scholar] [CrossRef] [PubMed] - Rusli, S.F.N.; Abu Bakar, M.H.; Loh, K.S.; Mastar, M.S. Review of high-performance biocathode using stainless steel and carbon-based materials in Microbial Fuel Cell for electricity and water treatment. Int. J. Hydrogen Energy
**2019**, 44, 30772–30787. [Google Scholar] [CrossRef] - Tsekouras, G.J.; Deligianni, P.M.; Kanellos, F.D.; Kontargyri, V.T.; Kontaxis, P.A.; Manousakis, N.M.; Elias, C.N. Microbial Fuel Cell for Wastewater Treatment as Power Plant in Smart Grids: Utopia or Reality? Front. Energy Res.
**2022**, 10, 843768. [Google Scholar] [CrossRef] - Sayed, E.T.; Alawadhi, H.; Olabi, A.G.; Jamal, A.; Almahdi, M.S.; Khalid, J.; Abdelkareem, M.A. Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater. Int. J. Hydrogen Energy
**2021**, 46, 5975–5983. [Google Scholar] [CrossRef] - Kurniawan, T.A.; Othman, M.H.D.; Liang, X.; Ayub, M.; Goh, H.H.; Kusworo, T.D.; Mohyuddin, A.; Chew, K.W. Microbial Fuel Cells (MFC): A Potential Game-Changer in Renewable Energy Development. Sustainability
**2022**, 14, 16847. [Google Scholar] [CrossRef] - Bala, S.; Garg, D.; Thirumalesh, B.V.; Sharma, M.; Sridhar, K.; Inbaraj, B.S.; Tripathi, M. Recent Strategies for Bioremediation of Emerging Pollutants: A Review for a Green and Sustainable Environment. Toxics
**2022**, 10, 484. [Google Scholar] [CrossRef] [PubMed] - Roy, H.; Rahman, T.U.; Tasnim, N.; Arju, J.; Rafid, M.M.; Islam, M.R.; Pervez, M.N.; Cai, Y.; Naddeo, V.; Islam, M.S. Microbial Fuel Cell Construction Features and Application for Sustainable Wastewater Treatment. Membranes
**2023**, 13, 490. [Google Scholar] [CrossRef] [PubMed] - Rezk, H.; Olabi, A.G.; Abdelkareem, M.A.; Sayed, E.T. Artificial intelligence as a novel tool for enhancing the performance of urine fed microbial fuel cell as an emerging approach for simultaneous power generation and wastewater treatment. J. Taiwan Inst. Chem. Eng.
**2023**, 148, 104726. [Google Scholar] [CrossRef] - Rezk, H.; Olabi, A.G.; Abdelkareem, M.A.; Maghrabie, H.M.; Sayed, E.T. Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production. Sustainability
**2023**, 15, 1878. [Google Scholar] [CrossRef] - Sayed, E.T.; Rezk, H.; Abdelkareem, M.A.; Olabi, A.G. Artificial neural network based modelling and optimization of microalgae microbial fuel cell. Int. J. Hydrogen Energy
**2023**. [Google Scholar] [CrossRef] - Abdollahfard, Y.; Sedighi, M.; Ghasemi, M. A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence. Sustainability
**2023**, 15, 1312. [Google Scholar] [CrossRef] - Garg, A.; Vijayaraghavan, V.; Mahapatra, S.S.; Tai, K.; Wong, C.H. Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Syst. Appl.
**2014**, 41 Pt 1, 1389–1399. [Google Scholar] [CrossRef] - Arslankaya, S. Comparison of performances of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) for estimating employee labor loss. J. Eng. Res.
**2023**, 100107. [Google Scholar] [CrossRef] - Zaghloul, M.S.; Hamza, R.A.; Iorhemen, O.T.; Tay, J.H. Comparison of adaptive neuro-fuzzy inference systems (ANFIS) and support vector regression (SVR) for data-driven modelling of aerobic granular sludge reactors. J. Environ. Chem. Eng.
**2020**, 8, 103742. [Google Scholar] [CrossRef] - Li, M.; Zhou, M.; Tian, X.; Tan, C.; McDaniel, C.T.; Hassett, D.J.; Gu, T. Microbial fuel cell (MFC) power performance improvement through enhanced microbial electrogenicity. Biotechnol. Adv.
**2018**, 36, 1316–1327. [Google Scholar] [CrossRef] [PubMed] - Nourbakhsh, F.; Pazouki, M.; Mohsennia, M. Simultaneous Investigation of Three Effective Parameters of Substrate, Microorganism Type and Reactor Design on Power Generation in a Dual-Chamber Microbial Fuel Cells. Iran. J. Biotechnol.
**2020**, 18, e2292. [Google Scholar] - Ramya, M.; Senthil Kumar, P. A review on recent advancements in bioenergy production using microbial fuel cells. Chemosphere
**2022**, 288, 132512. [Google Scholar] [CrossRef] - Koók, L.; Nemestóthy, N.; Bélafi-Bakó, K.; Bakonyi, P. The influential role of external electrical load in microbial fuel cells and related improvement strategies: A review. Bioelectrochemistry
**2021**, 140, 107749. [Google Scholar] [CrossRef] - Sharma, R.; Garg, P.; Kumar, P.; Bhatia, S.K.; Kulshrestha, S. Microbial Fermentation and Its Role in Quality Improvement of Fermented Foods. Fermentation
**2020**, 6, 106. [Google Scholar] [CrossRef] - Ucar, D.; Zhang, Y.; Angelidaki, I. An Overview of Electron Acceptors in Microbial Fuel Cells. Front. Microbiol.
**2017**, 8, 643. [Google Scholar] [CrossRef] [PubMed] - Lin, C.-W.; Chung, Y.-P.; Liu, S.-H.; Chen, W.T.; Zhu, T.-J. Optimizing the parameters of microbial fuel cells using response surface methodology to increase Cr(VI) removal efficiency and power production. Process Saf. Environ. Prot.
**2023**, 172, 369–378. [Google Scholar] [CrossRef] - Hidayat, A.R.P.; Widyanto, A.R.; Asranudin, A.; Ediati, R.; Sulistiono, D.O.; Putro, H.S.; Sugiarso, D.; Prasetyoko, D.; Purnomo, A.S.; Bahruji, H.; et al. Recent development of double chamber microbial fuel cell for hexavalent chromium waste removal. J. Environ. Chem. Eng.
**2022**, 10, 107505. [Google Scholar] [CrossRef] - Pedrycz, W. Interfaces of fuzzy models: A study in fuzzy information processing. Inf. Sci.
**1996**, 90, 231–280. [Google Scholar] [CrossRef] - Buragohain, M.; Mahanta, C. A novel approach for ANFIS modelling based on full factorial design. Appl. Soft Comput.
**2008**, 8, 609–625. [Google Scholar] [CrossRef] - Zhao, W.; Wang, L.; Zhang, Z. Artificial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm. Neural Comput. Appl.
**2020**, 32, 9383–9425. [Google Scholar] [CrossRef]

**Figure 5.**Three-dimensional plot of controlling parameters: [

**a**] power density and [

**b**] Cr(VI) removal efficiency.

**Figure 6.**Predicted versus measured data of the ANFIS model: [

**a**] power density and [

**b**] Cr(VI) removal efficiency.

**Figure 7.**Prediction accuracy of the ANFIS model: [

**a**] power density and [

**b**] Cr(VI) removal efficiency.

**Figure 8.**Particles convergence during parameter identification: [

**a**] cost function, [

**b**] normalized Cu(II)/Cr(VI) ratio, [

**c**] normalized substrate concentration, and [

**d**] normalized external resistance.

RMSE | Coefficient of Determination (R^{2}) | ||||
---|---|---|---|---|---|

Train | Test | All | Train | Test | All |

Model of power density | |||||

9.64 × 10^{−6} | 1.0598 | 0.4863 | 1.0 | 0.9864 | 0.9981 |

Model of Cr(VI) removal efficiency | |||||

2.63 × 10^{−5} | 2.1275 | 0.9761 | 1.0 | 0.9991 | 0.9963 |

Cu(II)/Cr(VI) Ratio | Substrate Concentration | External Resistance | Power Density, mW/m^{2} | Cr(VI) Removal Efficiency | Change in Power Density | Change in Cr(VI) Removal Efficiency | |
---|---|---|---|---|---|---|---|

Measured [32] | 1.4 | 1.45 (g/L) | 1000 Ω | 32.7 | 71% | 0.0 | 0.0 |

ANOVA [32] | 1.65 | 1.36 | 1360 | 33.84 | 71% | 3.48% | 0.0 |

ANFIS and AEO | 1.672 | 1.756 (g/L) | 1404.8 Ω | 38.96 | 81.75% | 19.14% | 15.14% |

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**MDPI and ACS Style**

Abdelkareem, M.A.; Alshathri, S.I.; Masdar, M.S.; Olabi, A.G.
Adaptive Neuro-Fuzzy Inference System Modeling and Optimization of Microbial Fuel Cells for Wastewater Treatment. *Water* **2023**, *15*, 3564.
https://doi.org/10.3390/w15203564

**AMA Style**

Abdelkareem MA, Alshathri SI, Masdar MS, Olabi AG.
Adaptive Neuro-Fuzzy Inference System Modeling and Optimization of Microbial Fuel Cells for Wastewater Treatment. *Water*. 2023; 15(20):3564.
https://doi.org/10.3390/w15203564

**Chicago/Turabian Style**

Abdelkareem, Mohammad Ali, Samah Ibrahim Alshathri, Mohd Shahbudin Masdar, and Abdul Ghani Olabi.
2023. "Adaptive Neuro-Fuzzy Inference System Modeling and Optimization of Microbial Fuel Cells for Wastewater Treatment" *Water* 15, no. 20: 3564.
https://doi.org/10.3390/w15203564