Virtual measurements in wastewater treatment plant - Machine learning models for predicting the PO4 concentration in the effluent

Aug 4, 2026

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Master’s thesis developing machine learning algorithms for effluent quality control in the HIAS wastewater treatment plant, targeting accurate prediction and quality classification of PO4 concentration using a time-series historical dataset. The study includes data preprocessing, feature preparation, model training, and systematic evaluation with quantitative metrics. Multiple supervised learning approaches are implemented and compared to determine effectiveness under wastewater process conditions. Results support improved decision-making for monitoring effluent quality and reducing uncertainty in virtual sensing.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages71

Reading Time2 h 59 min

AccessFree to download

Introduction

General

Process description

State of the Art

Theoretical background

Supervised learning

Cubic Spline Interpolation

Pearson correlation coefficient

Evaluation Metrics

Methodology and Materials

Data analytics for WWTP

Data preparation

Data Preprocessing

ACIT5900 MASTER THESIS in Applied Computer and Information Technology (ACIT) May 2023 Robotics and Control Virtual measurements in Wastewater treatment plant: Machine learning models for predicting the PO4 concentration in the effluent Mohamed Abdishakur Mohamed Department of ACIT Faculty of Technology, Art and Design Preface In the Name of Allah, the Most Gracious, the Most merciful. Praise be to Allah, the lord for all worlds, and may peace and blessings be upon His Messenger, Muhammad (peace be upon him) . It is with immense gratitude and humble commitment that I present this work, “Virtual measurement in wastewater treatment plant: Machine learning models for predicting the PO4 concentration in the effluent”, as part of my master ‘s thesis. This has been a journey of learning, discovery, and growth, and I acknowledge the blessings and guidance from Allah the Almighty throughout this project. The aim of this master’s thesis is to develop a comprehensive machine learning algorithms for effluent quality control for HIAS wastewater treatment plant. The study includes various machine learning algorithms. The aim is to assess effectiveness of these models in accurately predicting effluent parameter values and classifying effluent quality based on time-series historical dataset, from HIAS wastewater treatment plant. The work involves data preprocessing, model training, evaluation and validation through evaluation metrics. I would like to express my sincere appreciation to my supervisors, Arvind Keprate, Abhilash Nair and Tiina Komulainen, for their scholarly expertise and constant support. Their insightful feedback, and continuous support, and patient mentoring have been instrumental in shaping this work. I would also like to offer my sincere thanks to the members of my thesis group for their expertise, time, and valuable assistance in evaluating and reviewing this work. Their suggestions and constructive comments have greatly developed the quality of the research. Furthermore, I would like to express my deep gratitude to my family and friends for their support. Especially my parents, where their unwavering encouragement , love and understanding throughout this academic journey. Their moral support and prayers were a source of motivation for me. Thanks for believing in me. Finally, I acknowledge that this work would not have been possible without the guidance and blessing from Allah the Almighty. I am grateful for the knowledge granted to me. May this work serve as a means of seeking knowledge, benefitting humanity, and earning the pleasure of Allah, the most High. Table of Contents Table of Contents ........................................................................................................................................3 List of Figures ..............................................................................................................................................4 List of Tables................................................................................................................................................ 5 1 INTRODUCTION ........................................................................................................................................8 1.1 General .................................................................................................................................................8 1.2 Process description ...............................................................................................................................9 2 State of the Art ....................................................................................................................................... 10 3 Theoretical background .......................................................................................................................... 15 3.1 Supervised learning ..............................................

FAQs

What is the main goal of the thesis?

To develop machine learning algorithms that accurately predict effluent PO4 concentration and classify effluent quality for the HIAS wastewater treatment plant.

Which kind of data is used for training and evaluation?

A time-series historical dataset collected from the HIAS wastewater treatment plant is used for model training and validation.

What steps are included in building the models?

The workflow covers data preprocessing and preparation, model training, and evaluation/validation using defined evaluation metrics.

Aug 4, 2026

Upload by Asher