Performance Evaluation of Two Stage Wastewater Model for Treatment of Dairy Effluents
DOI:
https://doi.org/10.55863/ijees.2024.3122Keywords:
Dairy wastewater, Physicochemical analysis, Bioremediation, Microbial characterization, Effluent treatment, Sustainable wastewater treatmentAbstract
The continuous rise in consumer demand for dairy products has led to significant advancements in food and dairy technology, resulting in a substantial increase in dairy production. However, the expansion of the dairy industry has placed a strain on the environment due to the generation of waste products containing elevated levels of constituents such as casein, lactose, fat, inorganic salts, as well as detergents and sanitizers. These contribute to elevated Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD) values. While dairy facilities have implemented their own treatment plants to address these environmental challenges, there is an ongoing need to assess the efficiency of these wastewater treatment systems.
In this study, a two-stage model was developed, comprising a primary tank involving microbial treatment and a secondary tank with a layer of activated charcoal powder at the bottom and sawdust above it. The treated dairy effluent from these filtration units underwent analysis for various physicochemical parameters and was compared with samples obtained directly from the dairy industry. A total of eight indigenous bacterial isolates were derived from the processing of dairy wastewater through the model. Isolates R2 and R3 were obtained from raw wastewater, S1, S2, S3 isolates from secondary treated effluent, and T1, T2, T3 isolates from tertiary treated effluent. These isolates were further characterized biochemically as Micrococcus spp., Staphylococcus aureus, Bacillus spp., Staphylococcus spp., etc. Among all isolates, S3 demonstrated the highest reduction efficiency in water parameters, namely Electrical Conductivity, Chemical Oxygen Demand, and Biochemical Oxygen Demand, with reductions of 59.26%, 70.19%, and 74%, respectively
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Copyright (c) 2023 Sonika Saxena, Tejaswini Kumawat, Ramakant Lata, Jayana Rajvanshi, Sudipti Arora
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