Performance of a hybrid reactor system for the biological treatment of dye rich wastewater using textile effluent adapted microbial community

Abstract

This study explored sequential anoxic/aerobic (SAn/A) processes to enhance the efficiency and sustainability of textile wastewater treatment. We studied the simultaneous removals of color, COD, TKN, NO3-N, SO42−, and PO43−P; parameters that are rarely addressed collectively in the existing literature. We evaluated the performance of the SAn/A treatment process on dye-rich real textile wastewater using a textile effluent-adapted microbial community. Four phases (I-IV) were considered with organic loading rates (OLR) of 0.5, 1.0, 1.5, and 2.0 g COD/L/day. The SAn/A process achieved maximum color and COD removals of 95.6% and 87.6%, respectively, during Phase III (1.5 g COD/L/day). Phase II (1.0 g COD/L/day) exhibited the highest TKN (70.7%) and PO43−-P (55.2%) removal efficiencies, attributed to increased biomass. The anoxic stage achieved the highest color removal, whereas the aerobic stage improved TKN and PO43−-P removal. However, NO3-N concentrations increased during the aerobic stage due to ammonia oxidation, necessitating the development of nutrient management strategies. UV–visible and FTIR analyses indicated significant structural changes in the parent dyes, confirming the effectiveness of the SAn/A process. Overall, the SAn/A treatment process is effective and can help the textile industry meet environmental compliance in wastewater treatment. Hence, the novelty of the present study lies in its use of a textile-effluent–adapted microbial community to treat dye-rich real textile wastewater in a SAn/A reactor system under varying OLRs, providing realistic performance data, mechanistic insights into stage-specific pollutant removal, and spectroscopic confirmation of dye degradation. As the research was conducted at the laboratory scale, further work is required to optimize key experimental and environmental conditions and to test the system on a pilot scale to help clarify how the process performs and behaves under real-world conditions.