Advancements in Machine Learning for Estimating Parameters of Wastewater Treatment Plants - Model Development and Validation Using XGBoost

Aug 3, 2026

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The study develops and validates machine learning methods for calculating key parameters of aeration tanks in wastewater treatment plants during the technical and commercial proposal stage. It combines theoretical work based on fluid motion and reaction kinetics with enzymatic models for organic pollutant degradation, using machine learning and statistical decision theory. Laboratory experiments investigate wastewater sedimentation kinetics. Results include an XGBoost-based model that achieves high-accuracy optimization while improving efficiency and enabling safer, more adaptable design calculations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Motivation and regulatory context

Related computational approaches

Research results

Complexity of aeration tank design

Limits of classical empirical methods

Objectives, requirements, and expected outcomes

Advancements in machine learning for estimating parameters of wastewater treatment plants Natalya Kolyevaϭ*, Alexander Rastyagaev1, Lyudmila Kortenkoϭ, Sergey Rozhkovϭ͕ Mariia Sbitnevaϭ͕ and Aleksandr Kuznetsovϭ 1Ural State University of Economics, Ulitsa 8 Marta, 62/45, 620144, Yekaterinburg, Russian Federation Abstract. The aim of the study is to develop and validate machine learning methods for calculating the parameters of aeration tanks of wastewater treatment plants at the stage of technical and commercial proposal. Research methods included: generalization of known scientific and technical results, theoretical studies were conducted using the theory of fluid motion in the boundary layer, the theory of kinetics of enzymatic reactions of organic pollutants in wastewater, machine learning methods and statistical decision theory. Experimental studies were conducted on a laboratory setup to study the kinetics of wastewater sedimentation. As a result of the study, a model of the XGBoost algorithm was developed, which successfully coped with the task of optimization of calculations, providing high accuracy, and this, in turn, opens up new opportunities for improving the efficiency of design of wastewater treatment plants. 1 Introduction In recent years, machine learning (ML) methods have become actively applied in various fields, including ecology and engineering design. Machine learning is attractive due to its ability to analyze large amounts of data, identify complex dependencies and adapt the operation of individual pieces of equipment and systems as a whole to new conditions, which makes it a promising tool for optimizing calculations of aeration tank process parameters. The main document regulating the calculation of technological parameters of wastewater treatment facilities in the Russian Federation is the Code of Rules. The updated version [1-3], provides designers with an opportunity to use alternative methods of calculation of biological treatment facilities, including mathematical models. This opens new horizons for engineers, but at the same time poses them a difficult task – the choice of methodology in order to minimize risks and ensure the required quality of wastewater treatment. This issue becomes especially relevant in the design of aeration tanks . In 2018-2019, papers were published in [4-6], which made a significant contribution to the development of computational methods. In recent years, the controversy around this * [Corresponding author: nkoleva@mail.ru](Corresponding author: nkoleva@mail.ru) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). BIO Web of Conferences 173, 03013 (2025) [https://doi.org/10.1051/bioconf/202517303013](https://doi.org/10.1051/bioconf/202517303013) [AFE-2024](AFE-2024) topic has only intensified: experts analyze existing approaches, discuss international experience and propose their own solutions. In this context, special attention should be paid to the studies [7], which provide an in-depth analysis of aeration basin calculation methods, including ATV-DVWK-A131E, Danilovich-Epov methodology and ASM2d. The authors conclude that the most correct methods are those based on enzyme kinetics formulas. However, the choice of a specific approach is left to the technologist, who should take into account the specifics of the project and the requirements for the quality of treated water. 2 Research results The design of aerobic wastewater treatment plants, particularly aeration tanks, is a complex and multi-stage process that requires consideration of many factors such as wastewater composition, activated sludge load, oxygen concentration, temperature conditions and other parameters. Traditional calculation methods based on empirical formulas and regulations have

FAQs

What is the main goal of the study?

To develop and validate machine learning methods for calculating aeration tank parameters during the technical and commercial proposal stage, targeting higher accuracy and speed.

Which machine learning model was developed?

An XGBoost algorithm model was developed to optimize calculations with high accuracy.

What experimental and theoretical methods support the research?

The work includes theoretical studies using fluid motion in the boundary layer and kinetics of enzymatic reactions, plus laboratory experiments to study wastewater sedimentation kinetics.

Aug 3, 2026

Upload by Skyler