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. 2025 Jan 19;12(1):88.
doi: 10.3390/bioengineering12010088.

Metagenomic Insights into Pollutants in Biorefinery and Dairy Wastewater: rDNA Dominance and Electricity Generation in Double Chamber Microbial Fuel Cells

Khaya Pearlman Shabangu  1   2 , Manimagalay Chetty  1   3 , Babatunde Femi Bakare  2
Affiliations

Metagenomic Insights into Pollutants in Biorefinery and Dairy Wastewater: rDNA Dominance and Electricity Generation in Double Chamber Microbial Fuel Cells

Khaya Pearlman Shabangu et al. Bioengineering (Basel). .

Abstract

This study evaluates the potential of biorefinery and dairy wastewater as substrates for electricity generation in double chamber Microbial Fuel Cells (DCMFC), focusing on their microbial taxonomy and electrochemical viability. Taxonomic analysis using 16S/18S rDNA-targeted DGGE and high-throughput sequencing identified Proteobacteria as dominant in biorefinery biomass, followed by Firmicutes and Bacteriodota. In dairy biomass, Lactobacillus (77.36%) and Clostridium (15.70%) were most prevalent. Biorefinery wastewater exhibited the highest bioelectrochemical viability due to its superior electrical conductivity and salinity, achieving a voltage yield of 65 mV, compared to 75.2 mV from mixed substrates and 1.7 mV from dairy wastewater. Elevated phosphate levels in dairy wastewater inhibited bioelectrochemical processes. This study recommends Biorefinery wastewater as the most suitable purely organic substrate for efficient bioelectricity generation and scaling up of MFCs, emphasising the importance of substrate selection for optimal energy output for practical and commercial viability.

Keywords: Bacteriodota; Firmicutes; Microbial Fuel Cell; Proteobacteria; electrical conductivity; salinity; wastewater.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses or interpretation of data, in the writing of the manuscript or in the decision to publish the results.

Figures

Figure 1
Figure 1
(A) Schematic view showing upstream influent discharge plants to the onsite wastewater pretreatment plant and influent sample harvest points for a local biorefinery plant. (B) Schematic view for upstream influent discharge plants to onsite Dairy Plant.
Figure 2
Figure 2
Experimental setup for the DCMFC benchtop for this study, created in CardWorx Professional [22,23]. * Refers to an optional data logger system. The process can be conducted with or without a data logger unit.
Figure 3
Figure 3
Statistical methodology approach in R-software [14,24,25].
Figure 4
Figure 4
(A): Correlation chart from R, for chemical/organic parameters. (B): Correlation chart from R, for pysicochemical parameters on the wastewater streams.
Figure 5
Figure 5
TOC vs. COD for dairy wastewater. (a) Dairy wastewater. (b) Biorefinery wastewater. (c) Mixed wastewater.
Figure 6
Figure 6
(a) Resistivity comparisons against all streams. (b) Salinity comparisons, against all streams.
Figure 7
Figure 7
(a) ORP vs. pH for Clover wastewater. (b) TDS vs. Salinity for biorefinery wastewater. (c) ORP vs. pH for mixed wastewater.
Figure 8
Figure 8
Electrical conductivity comparison plots, all streams.
Figure 9
Figure 9
Top-phylum taxonomy classification for a biorefinery wastewater stream.
Figure 10
Figure 10
Top-Genus taxonomical classification for dairy wastewater stream.
Figure 11
Figure 11
CCV—yield comparisons between all three-streams. (a) Biorefinery stream. (b) Mixed wastewater stream. (c) Dairy wastewater stream, Shabangu et al. [70,71].
See this image and copyright information in PMC

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