Electricity Generation and Wastewater Treatment with Membrane-Less Microbial Fuel Cell

Tahir, Chenar A.; Pásztory, Zoltán; Agarwal, Charu; Csóka, Levente

doi:10.1007/978-981-16-2225-0_8

Cited by 5 publications

(1 citation statement)

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“…The catholyte improves the cathodic chamber's bioelectrochemical capacity and the mechanism of the overall flow of electrons from the anodic chamber to the highly efficient cathodic chamber seeded with a potassium permanganate electron acceptor [48]. Thus, the desirability is increased by an increase in catholyte dosage to the maximum concentration of 0.1 mg/L [48,49]. However, other factors, such as the instability of intracellular adenosine triphosphate content and low microbial activity or biomass ratio, should be taken into consideration [50] as they reduce the performance of DCMFCs, as reported by Shabangu et al [15,43,48,[51][52][53][54][55].…”

Section: Numerical Optimization Based On Ramp Plots For Desirability ...mentioning

confidence: 99%

Optimization and Modeling of a Dual-Chamber Microbial Fuel Cell (DCMFC) for Industrial Wastewater Treatment: A Box–Behnken Design Approach

Shabangu,

Chetty,

Bakare

2024

Energies

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Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % PO43−—the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation.

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