Phosphorus recovery directly from sewage sludge as vivianite and simultaneous electricity production in a dual chamber microbial fuel cell

Wang, Lingjun; Tai, Yanfeng; Zhao, Xin; He, Qinqin; Hu, Zhenzhen; Li, Min

doi:10.1016/j.jece.2023.110152

Cited by 4 publications

(2 citation statements)

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“…Among these, Response Surface Methodology (RSM) stands out as a widely favored approach [1]. Various optimization designs are at researchers' disposal, including full-factorial, response surface (such as central composite and Box-Behnken designs), optimal, and mixture designs (like simplex lattice and simplex centroid designs) [4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Some well-known commercial packages for this purpose encompass Minitab, Statistica, SPSS, SAS, Design-Expert, Stat-graphics, Prisma, MODDE, DoE Fusion PRO, STAVEX, and more. Engaging in any DoE experiment necessitates a grasp of statistical methods such as ANOVA and linear regression, as these are foundational in the process [4,5,7,12,[18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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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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