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Enhancing Sustainability in the Textile Industry: Employing Small-Scale Physical Model to Enhance Wastewater Treatment Aeration Efficiency through Influent Temperature Regulation.
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1  North South University
Academic Editor: Wataru Takeuchi

Abstract:

Bangladesh holds the position of being the world's second-largest apparel exporter, boasting
approximately 4,500 textile industries. However, these textile operations, heavily reliant on water for
various processes, generate a significant volume of wastewater. To ensure the responsible discharge
of this wastewater and prevent environmental contamination, it is imperative to have effluent
treatment plants (ETPs) in place. One of the key challenges in ETP operations is the aeration
process, which is not only crucial but also consumes a substantial portion of the plant's total energy,
ranging from 50% to 90%. This heightened energy consumption is partly due to the elevated
temperature of the influent wastewater, which results in a lower Oxygen Transfer Rate (OTR),
necessitating more aeration. To tackle this issue, a model has been developed to address the
problem by employing a cooling technique to reduce the temperature of the influent wastewater. This
is achieved by creating a turbulent environment within the system. The effectiveness of this model
was assessed by measuring temperature reductions at three different flow rates. The results of the
study indicate that lowering the temperature of the influent wastewater leads to an increase in the
OTR value. Specifically, the model's output includes a graphical representation of OTR and
temperature data, revealing a strong correlation between the two variables. The high R-squared value
of 0.9579 confirms the reliability of this trend line. By implementing measures to reduce the
temperature of incoming wastewater, it is possible to establish a more sustainable and
environmentally responsible textile manufacturing industry.

Keywords: Aeration; Environment; Oxygen Transfer Rate; Wastewater
Comments on this paper
Miakosl Lians
Reducing temperatures at three distinct flow rates allowed us to evaluate the model's efficacy.
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