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This research aims to model the performance of a sewage treatment plant using advanced algorithms to enhance treatment outcomes.

February 8, 2026

Performance Modelling of Sewage Treatment Plant by the Application of Polynomial Neural Network

Key Points

This research aims to model the performance of a sewage treatment plant using advanced algorithms to enhance treatment outcomes.
Evaluated sewage treatment performance using a Polynomial Neural Network algorithm.
Analyzed 6 datasets per parameter across three treatment stages: Inlet, Primary Stage Treatment, Final Stage Treatment.
Utilized cross-validation and calculated Mean Absolute Percentage Error for model validation.
PNN achieved high prediction accuracies (R2 > 0.95) for BOD, COD, and TSS removal efficiencies.
Reductions observed were 87% for BOD, 90% for COD, and 94% for TSS.
Identified need for model refinement in parameters like pH and Oxygen Absorption due to low variability.

Abstract

This study quantitatively evaluated and modelled the performance of a sewage treatment plant (STP) using a Polynomial Neural Network (PNN) algorithm, an advanced machine learning approach, to predict effluent quality and optimize treatment efficiency in Ghaziabad, India. The PNN algorithm achieved prediction accuracies (R 2 > 0.95) for Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Total Suspended Solids (TSS) removal efficiencies, demonstrating reductions of 87%, 90%, and 94%, respectively. A total of 6 datasets per parameter were collected and analyzed across three treatment stages (Inlet, Primary stage treatment (PST), Final stage treatment (FST)). Cross-validation and Mean Absolute Percentage Error (MAPE < 5%) confirmed model robustness. The model required refinement for parameters with low variability (pH, Oxygen Absorption (OA), Total alkalinity), highlighting chemical dose nonlinearity. This algorithmic insight supports sustainable wastewater management practices by ensuring compliance with BIS discharge standards.

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