Elsevier

Bioresource Technology

Volume 297, February 2020, 122397
Bioresource Technology

Elucidating the effect of mixing technologies on dynamics of microbial communities and their correlations with granular sludge properties in a high-rate sulfidogenic anaerobic bioreactor for saline wastewater treatment

https://doi.org/10.1016/j.biortech.2019.122397Get rights and content

Highlights

Abstract

In this study, three lab-scale anaerobic sulfidogenic bioreactors were operated independently using three different mixing modes (hydraulic, mechanical and pneumatic). One-way ANOVA test indicated various performance parameters (e.g. sulfate reduction and sulfide production) and granular sludge properties (e.g. EPS and particle size) statistically different in three mixing modes. Principal component analysis (PCA) and OTUs-based network demonstrated bacterial composition greatly varied among the three mixing modes. The phylum Proteobacteria was predominant among the bacterial communities, and the genus Desulfobacter (35.1% in hydraulic, 31.1% in mechanical and 27.4% in pneumatic sample) was the most dominant SRB. The PCA/Pearson’s correlation analysis confirmed SRB had significant positive relationship with sludge properties (e.g. particle size). PICRUSt data highlighted that bacterial communities contained diverse predicted functions including sulfur metabolism enzymes (sulfite reductase and adenylylsulfate reductase). The findings of this research could be helpful for selection of an appropriate mixing technology for anaerobic sulfidogenic or similar bioprocess.

Introduction

Due to use of seawater for toilet flushing in Hong Kong, saline sewage is generated, and it contains nearly an average amount of 5,000 mg/L chloride and 504 mg/L sulfate (Wang et al., 2011). To treat sulfur containing saline wastewater, a novel bioprocess called the Sulfate Reduction, Autotrophic Denitrification, Nitrification Integrated (SANI®) process has been developed from lab-scale to full-scale application (Wu et al., 2016). In the SANI® process, an anaerobic sulfidogenic bioreactor (also called the sulfate-reducing up-flow sludge bed (SRUSB)) acts as the main reactor for organic removal (more than 80% of chemical oxygen demand (COD)) as well as to produce enough dissolved sulfide for subsequent utilization in autotrophic denitrification. The main reactions involved in the biological sulfate reduction in SRUSB by incomplete and complete organic (e.g. glucose, C6H12O6) oxidizng sulfate reducing bacteria (Liamleam and Annachhatre, 2007) are presented in the following equations.C6H12O6(Glucose)+SO42-HS-+2CH3COO-(Acetate)+2HCO3-+3H+ΔG0:-358.2kJC6H12O6(Glucose)+3SO42-3HS-+6HCO3-+3H+ΔG0:-452.5kJ

However, it has been observed that the performance of the SRUSB bioreactor is significantly impacted due to washing-out of sludge (associated biomass) by unforeseen sludge flotation behavior (van Lier et al., 2016, Wang et al., 2017). Several studies have been conducted to find out the possible reasons about sludge floatation behavior for treatment of high-strength industrial wastewater in anaerobic bioreactor, e.g. upflow anaerobic sludge blanket (UASB) and expanded granular sludge bed (EGSB) are the most common configurations. It is reported that the sludge granulation can be impacted by a number of process operational related as well as external factors such as hydraulic retention time, volumetric loading rate, temperature, pH, alkalinity, upflow velocity, presence of heavy metals, salinity and nutrient availability (Campos et al., 2017, McHugh et al., 2003, Tiwari et al., 2006, van Lier et al., 2016). Campos et al., reported that floatation of granulation can be prevented by increasing operational temperature (above 25 °C). Presence of multivalent ions (Ca2+ and Fe2+) is advantageous since they reduce electrostatic repulsion between negatively charged bacteria, and promote anaerobic granulation (McHugh et al., 2003). According to Tiwari et al., the pH, alkalinity, and organic loading rate (OLR) are closely related parameters that could affect sludge granulation (Tiwari et al., 2006). High OLR can cause decrease of pH by production of volatile fatty acids (VFAs). In addition to wastewater qualities and operations condition, reactor configurations (UASB and EGBS) could impact the degree of sludge granulation (van Lier et al., 2016). Moreover, the nature of seeding sludge and chemical composition of wastewater can remarkably impact properties and stability of the granular sludge (Batstone et al., 2004). Nonetheless, some studies have pointed out that the gaseous by-products generated in the anaerobic bioreactor due to co-occurrence of other bioprocesses including methane (CH4) from methanogenesis, carbon dioxide (CO2) from heterotrophic denitrification, and hydrogen sulfide (H2S) from biological sulfate reduction also impact sludge granulation. Researchers have suggested that installation of an adequate mixing system that could provide an appropriate shear force can reduce the sludge floatation potential (Lee et al., 2006, Li et al., 2014, Ma et al., 2011). But only a few studies are conducted to examine the impact of various mixing methods on the performance of anaerobic bioreactor.

Recently, a study has investigated the impact of mixing modes and mixing intensities on the performance including sludge floatation potential (SFP) in SRUSB (Wang et al., 2017). In Wang et al. (2017), three SRUSBs were tested in parallel with three different mixing modes such as hydraulic (effluent re-circulation), mechanical (using a mechanical mixture) and pneumatic mixing (employing a continuous gas recirculation system) for 180 days. The authors have found that pneumatic was the best mixing mode since it significantly reduces the SFP by 64% and 42% compared to the hydraulic and mechanical mixing. Furthermore, pneumatic mixing sludge particles were bigger (1.2 – 1.7 times larger than others), and sludge physicochemical properties were improved. Finally, in terms of performance, pneumatic mixing technique showed similar or better COD removal efficiency, sulfate reduction and sulfide production. Although the findings of this study provide the basic understanding on the impact of various mixing technologies on SRUSB bioreactors, the information regarding the microbial dynamics (specifically bacteria which metabolize sulfur), and their correlations with reactor performance and granular sludge properties is still unclear, thus need investigation.

In sulfidogenic anaerobic bioreactor, the presence of sulfate emboldens the growth of sulfate reducing bacteria (SRB) which are responsible for the biological sulfate reduction (Lens and Kuenen, 2001, Lu et al., 2011). In order to enhance the treatment efficiency of the bioprocess through augmentation of SRB activities, it is highly essential to understand the diversity of microbial community composition and their abundance in the SRUSB treating the saline sewage. To date, only two studies are conducted using fluorescence in situ hybridization (FISH) and pyrosequencing-based molecular methods to investigate the microbial community profile in the SANI-SRUSB bioreactor. FISH-based analysis showed Thermotogales-like and Desulforhopalus-like SRB were found in the SRUSB sludge (Wang et al., 2011), whereas pyrosequencing analysis revealed that diverse SRB genera such as Desulfobulbus (18.1%), Desulfobacter (13.6%), Desulfomicrobium (5.6%), Desulfosarcina (0.73%) and Desulfovibrio (0.6%) were enriched in sulfate-reducing granules (Hao et al., 2013). However, to our knowledge, no study has published yet about how mixing technologies impact the SRB community in SRUSB.

Therefore, the principal objective of this study was to assess how various mixing techniques affect on the microbial community composition, function, and their correlations with granular sludge characteristics. This paper investigated the aforementioned questions by conducting lab-scale test in SRUSBs employing hydraulic, mechanical and pneumatic mixing technologies. Microbial and chemical characteristics of granular sludge samples were analyzed. Moreover, one-way ANOVA test and redundancy analysis (RDA) were conducted to statistically evaluate the impact of mixing modes on SRUSB sludge qualities. The principal component analysis (PCA), Pearson’s correlation and OTUs-based network analysis were performed to find possible relationships between dynamics of SRB communities and sludge physicochemical properties. The findings of this study would be helpful for advancing mixing technologies in the SANI-based SRUSB system.

Section snippets

Sulfate-reducing upflow sludge bed (SRUSB) reactor operation

Three lab-scale SRUSB reactors were built with transparent polyacrylic having a height of 85 cm and an internal diameter of 6 cm with the working volume of each reactor was 2.4 L. The reactors were installed with apparatus to provide hydraulic, mechanical and pneumatic mixing. Effluent recirculation was adopted to initiate hydraulic mixing. Lab-scale overhead mixer with a 4 cm-wide two-blade axial-flow impeller (OS20-S, DragonLab, China) was mounted to provide mechanical mixing. A continuous

Effect of mixing modes on reactor permance and granular sludge proporties

The detail information about how the different mixing modes with changing mixing intensities effect the SRUSB bioreactor performance and sludge properties has been reported in our previous paper (Wang et al., 2017). In the context of current study a few key parameters were discussed with in-depth statistical analysis.

A total of 28 performance-related parameters measured during the operation of three reactors were summarized in the Table 1 of this paper. Table 2 elucidates the summary of sludge

Conclusions

In this work, three SRUSBs were operated employing hydraulic, mechanical and pneumatic mixing, and the following important conclusions were drawn: overall microbial community including SRB considerably change by mixing modes. The genera Desulfobacter was the dominant SRB, and PCA/Pearson’s correlation commutation confirmed SRB had significant positive correlation with a few parameters namely granular sludge particle size and COD removal efficiency. PICRUSt data highlighted abundance of several

Acknowledgements

The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (project no. [T21-604/19-R]). The authors also gratefully acknowledge the support of the Hong Kong Innovation and Technology Commission (grant no. ITC-CNERC14EG03), the National Natural Science Foundation of China (grant no. 51638005), Shenzhen Science and Technology Innovation Committee Project (grant no. JSGG2017101071620730).

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