Optimal Design of Water Treatment Plants using Genetic Programming

Constant effluent condition forecasting in wastewater treatment systems is critical for actively reducing environmental and wellness concerns. Nevertheless, wastewater treatment is a very complicated procedure governed by a number of unpredictable, interrelated, and occasionally imperfectly understood physicochemical-biological variables. Furthermore, there are significant spatiotemporal changes, ambiguity, and strong nonlinear connections between the water quality metrics and the procedure factors associated with the treatment procedure. Such complications impede the effective surveillance, functioning, and administration of wastewater treatment plants under both standard and abnormal settings. Predicting wastewater treatment plants (WWTP) influence variables, like 5-day biological oxygen demand (BOD5) and chemical oxygen demand (COD), is critical for optimum energy and power usage. Despite this background, the current collection of information lacks an investigation that uses Artificial Intelligence (AI)-based methodologies to forecast BOD5 and COD. In this research, genetic programming (GP), and other AL algorithms have been developed for estimating BOD5 and COD employing monthly data gathered from the incoming flows of 7 WWTPs in India over three years. Depending on many quantitative factors, GP produces more precise estimates, with R2 scores of 0.785 and 0.862 for BOD5 and COD, respectively. Additionally, the outcomes of sensitivity evaluation using a Monte Carlo model showed that overall suspended solids levels had a major impact on both BOD5 and COD, with a ten percent rise in TSS score leading to a 7.95% and 7.93% raise in BOD5 and COD measurements, respectively. The GP simulation findings were found to comply with the fundamental science of the wastewater standards and to apply to leachate and additional wastewater resources, including industrialized sewage. The encouraging outcomes open the door to predicting the operating variables throughout sludge analysis, which will end up in significant power funding throughout the wastewater treatment procedures.

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Publication Details

Published
2025-08-04
DOI
https://doi.org/10.1109/icscsa66339.2025.11170746
Primary Topic
Adsorption and biosorption for pollutant removal
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article
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Optimal Design of Water Treatment Plants using Genetic Programming

Adsorption and biosorption for pollutant removal
article

Optimal Design of Water Treatment Plants using Genetic Programming

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Abstract

Constant effluent condition forecasting in wastewater treatment systems is critical for actively reducing environmental and wellness concerns. Nevertheless, wastewater treatment is a very complicated procedure governed by a number of unpredictable, interrelated, and occasionally imperfectly understood physicochemical-biological variables. Furthermore, there are significant spatiotemporal changes, ambiguity, and strong nonlinear connections between the water quality metrics and the procedure factors associated with the treatment procedure. Such complications impede the effective surveillance, functioning, and administration of wastewater treatment plants under both standard and abnormal settings. Predicting wastewater treatment plants (WWTP) influence variables, like 5-day biological oxygen demand (BOD5) and chemical oxygen demand (COD), is critical for optimum energy and power usage. Despite this background, the current collection of information lacks an investigation that uses Artificial Intelligence (AI)-based methodologies to forecast BOD5 and COD. In this research, genetic programming (GP), and other AL algorithms have been developed for estimating BOD5 and COD employing monthly data gathered from the incoming flows of 7 WWTPs in India over three years. Depending on many quantitative factors, GP produces more precise estimates, with R2 scores of 0.785 and 0.862 for BOD5 and COD, respectively. Additionally, the outcomes of sensitivity evaluation using a Monte Carlo model showed that overall suspended solids levels had a major impact on both BOD5 and COD, with a ten percent rise in TSS score leading to a 7.95% and 7.93% raise in BOD5 and COD measurements, respectively. The GP simulation findings were found to comply with the fundamental science of the wastewater standards and to apply to leachate and additional wastewater resources, including industrialized sewage. The encouraging outcomes open the door to predicting the operating variables throughout sludge analysis, which will end up in significant power funding throughout the wastewater treatment procedures.

Urgench State University (UZ), Jaypee Institute of Information Technology (IN), Rama University (IN), University of Petroleum and Energy Studies (IN), Dehradun Institute of Technology University (IN)
Clean water and sanitation
Openalex Percentile: Top 48%
Adsorption and biosorption for pollutant removal
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