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Enhancing Wastewater Stabilization Ponds for Treating Domestic Wastewater Using Machine Learning
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Author(s): T. Venkat Narayana Rao (Sreenidhi Institute of Science and Technology, India), Shiva Kashyap Yellavajhala (Sreenidhi Institute of Science and Technology, India), Muddasani Harshith (Sreenidhi Institute of Science and Technology, India)and K. Siva Kumar Gowda (Sreenidhi Institute of Science and Technology, India)
Copyright: 2025
Pages: 28
Source title:
Practical Applications of Machine Learning and AI: Medicine, Environmental Science, Transportation, and Education
Source Author(s)/Editor(s): Toufik Mzili (Chouaib Doukkali University, Morocco)and Adarsh Kumar Arya (Harcourt Butler Technical University, India)
DOI: 10.4018/979-8-3373-1399-3.ch006
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Abstract
In the realm of wastewater treatment, Wastewater Stabilization Ponds (WSPs) are highly regarded for their efficiency, particularly when incorporating baffle walls (BWs) into their design. These BWs play a crucial role in minimizing space requirements and improving contaminant removal, albeit at the expense of increased construction material usage. To find the best configuration, a thorough analysis was first carried out utilizing typical methodology (TM) design worksheets for WSPs. This phase validated the effectiveness of BWs by showing a considerable reduction in both WSPs area and hydraulic retention time (RT). Within the MM framework, additional reductions in WSPs area and RT were attained, coupled with a 5% drop in necessary concrete volume compared to TM, by employing the interior-point approach in MATLAB and the generalized reduced gradient (GRG) algorithm in MS Excel Solver. While these findings highlight the effectiveness of MM in optimizing WSPs, there is potential for exploring alternative algorithms to achieve more favorable results.
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