Microbiological insights into the haloalkaline biodesulfurization process

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Abstract Summary

Biodesulfurization (BD) under haloalkaline conditions removes H2S from gas streams and converts it to elemental sulfur by using haloalkaliphilic sulfur-oxidizing bacteria (SOB). In this process, gas is first absorbed in an absorber and oxidized into sulfur in an aerated bioreactor. Recently, the BD process improved with an anoxic bioreactor placed between the absorber and the oxic bioreactor to minimize the unwanted production of sulfate and thiosulfate1. However, microbial activities affect the product selectivity directly, and therefore it is needed to study the microbiology involved in the updated process line-up.

This study aimed to analyse the diversity of the microbial community in the BD process by determining the active players and study their ecophysiology. For this, biomass was obtained from six different month-long operational runs of a pilot-scale BD plant, each corresponding to different operational conditions. A complementary approach of DNA and RNA-based 16SrRNA gene amplicon sequencing was used to determine the active and inactive fraction of the microbial community of each run.

16SrRNA-based amplicon sequence analyses with QIIME2 indicated significant differences in the active and non-active microbial populations, suggesting that only part of the population is active. The composition of the active community was consistent but differentially abundant in all six operations. Contrarily, the composition of the non-active fraction varied among runs. The microbial community composition also changed within the duration of each run. There was no difference in microbial diversity in the different sections of the system. 

The remarkable differences in active and non-active bacterial populations indicate that RNA-based microbial composition analysis is essential to determine the actual key players and their activity in the haloalkaline BD process. The next step is to determine the correlations with essential process parameters and use the information to better understand and control the bioprocess.

Abstract ID :
MEWE117
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Wetsus/UvA
Wetsus/Paqell
Wageningen University & Research/Wetsus

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