Wastewater treatment and microbial communities in an integrated photo-bioelectrochemical system affected by different wastewater algal inocula

Xiao, Li; Young, Erica B.; Grothjan, Jacob J.; Lyon, Stephen; Zhang, Husen; He, Zhen

doi:10.1016/j.algal.2015.10.008

Cited by 34 publications

(17 citation statements)

References 49 publications

(59 reference statements)

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“…This genus was only identified using the MP Bio extraction kit, suggesting the possible role of extraction techniques in bacterial community composition analysis. It should be noted that algal reference sequences are not typically as well represented as bacteria in 16S rRNA sequence databases, and that even more careful targeting (e.g., 18S rRNA), could lead to large proportions of unidentified sequences (Xiao et al, 2015).…”

Section: Microbial Communities In the Hrapsmentioning

confidence: 99%

Microbial community structures in high rate algae ponds for bioconversion of agricultural wastes from livestock industry for feed production

Ibekwe

Murinda

Murry

et al. 2017

Science of The Total Environment

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Dynamics of seasonal microbial community compositions in algae cultivation ponds are complex. However, there is very limited knowledge on bacterial communities that may play significant roles with algae in the bioconversion of manure nutrients to animal feed. In this study, water samples were collected during winter, spring, summer, and fall from the dairy lagoon effluent (DLE), high rate algae ponds (HRAP) that were fed with diluted DLE, and municipal waste water treatment plant (WWTP) effluent which was included as a comparison system for the analysis of total bacteria, Cyanobacteria, and microalgae communities using MiSeq Illumina sequencing targeting the 16S V4 rDNA region. The main objective was to examine dynamics in microbial community composition in the HRAP used for the production of algal biomass. DNA was extracted from the different sample types using three commercially available DNA extraction kits; MoBio Power water extraction kit, Zymo fungi/bacterial extraction kit, and MP Biomedicals FastDNA SPIN Kit. Permutational analysis of variance (PERMANOVA) using distance matrices on each variable showed significant differences (P=0.001) in beta-diversity based on sample source. Environmental variables such as hydraulic retention time (HRT; P<0.031), total N (P<0.002), total inorganic N (P<0.002), total P (P<0.002), alkalinity (P<0.002), pH (P<0.022), total suspended solid (TSS; P<0.003), and volatile suspended solids (VSS; P<0.002) significantly affected microbial communities in DLE, HRAP, and WWTP. Of the operational taxonomic units (OTUs) identified to phyla level, the dominant classes of bacteria identified were: Cyanobacteria, Alpha-, Beta-, Gamma-, Epsilon-, and Delta-proteobacteria, Bacteroidetes, Firmicutes, and Planctomycetes. Our data suggest that microbial communities were significantly affected in HRAP by different environmental variables, and care must be taken in extraction procedures when evaluating specific groups of microbial communities for specific functions.

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Section: Microbial Communities In the Hrapsmentioning

confidence: 99%

Microbial community structures in high rate algae ponds for bioconversion of agricultural wastes from livestock industry for feed production

Ibekwe

Murinda

Murry

et al. 2017

Science of The Total Environment

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“…Actually, the cathode contains mixed population of algae and bacteria, and the synergistic performance of algal-bacterial consortium is important for IPB performance [37]. For example, some algal taxa (e.g., Chlamydomonas reinhardtii, Chlorella vulgaris) have strong tolerance ability to treat various wastewater and a fast growth rate [38], while bacterial taxa Rubrimonas cliftonensis was reported to be capable of nitrate reduction [14]. Studying the working mechanism of these species will help not only the improvement of IPB performance but also the promotion of model functions to give better predication on selecting suitable microbial species.…”

Section: Perspectivementioning

confidence: 99%

“…It was found that 92.4% soluble COD (chemical oxygen demand), 63% total nitrogen (TN) and 82.3% total phosphorus (TP) could be removed, and the energy production (including electricity and biomass biofuel) could theoretically compensate for energy consumption of the IPB system [9]. The algal-bacterial community in the cathode can help nutrient removal in IPB, and certain photoautotrophs (e.g., Leptolyngbya) or chlorophytes (e.g., Acutodesmus) were found as significant species in the community [14].…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of the dynamic behavior of an integrated photo-bioelectrochemical system for simultaneous wastewater treatment and bioenergy recovery

Luo

Wang

2017

Energy

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An integrated photo-bioelectrochemical (IPB) system is innovative through integrating microbial fuel cells (MFCs) with algal bioreactors for simultaneous organics degradation, nutrient removal, and bioenergy production. A mathematical model has been developed for simulating and understanding the performance of the IPB system. The model inputs include influent COD (chemical oxygen demand), NH 4 +-N, total phosphorus, external resistance and flow rate, while the outputs include the biomass growth, COD degradation, nutrient removal, and electricity generation. The determination of unknown model parameters was assisted with sensitivity analysis. Satisfactory model fitting and validation was achieved, with low root-mean-square error of 5.6 % and 0.2 %, respectively, for biomass concentration and current generation under varied COD input. The simulated results suggested that the organic input and flow rate had more significant impacts on the growth of algal biomass than other input factors, while COD, flow rate and external resistance were of importance for current generation. The optimal condition for improving this particular IPB system was predicted to have a COD concentration above 150 mg L-1 and the flow rate at 0.1 mL min-1. This IPB model is the first attempt of the kind for the optimization of an integrated bioprocess of electrochemical reactions and algal growth.

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“…Sensitivity analysis can also assist in parameter tuning for accelerated model construction. Other innovated applications in BES also need new parameters to analyze the system performance, such as growth rate of algal biomass for integrated photo-bioelectrochemical system (IPB), and the constant for hydrogen production rate in microbial electrolysis and desalination cells (MEDC) [120][121][122].…”

Section: Sensitivity Analysis Of Model Parametersmentioning

confidence: 99%

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

et al. 2016

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Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

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Wastewater treatment and microbial communities in an integrated photo-bioelectrochemical system affected by different wastewater algal inocula

Cited by 34 publications

References 49 publications

Microbial community structures in high rate algae ponds for bioconversion of agricultural wastes from livestock industry for feed production

Microbial community structures in high rate algae ponds for bioconversion of agricultural wastes from livestock industry for feed production

Mathematical modeling of the dynamic behavior of an integrated photo-bioelectrochemical system for simultaneous wastewater treatment and bioenergy recovery

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

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