To effectively operate and manage the complex wastewater treatment system, simplified representations, known as wastewater modeling, are critical. Wastewater modeling allows for the understanding, monitoring, and prediction of wastewater treatment processes by capturing intricate relationships within the system. Process-driven models (PDMs), which rely on a set of interconnected hypotheses and assumptions, are commonly used to capture the physical, chemical, and biological mechanisms of wastewater treatment. More recently, with the development of advanced algorithms and sensor techniques, data-driven models (DDMs) that are based on analyzing the data about a system, specifically finding relationships between the system state variables without relying on explicit knowledge of the system, have emerged as a complementary alternative. However, both PDMs and DDMs suffer from their limitations. For example, uncertainties of PDMs can arise from imprecise calibration of empirical parameters and natural process variability. Applications of DDMs are limited to certain objectives because of a lack of high-quality dataset and struggling to capture changing relationship. Therefore, this dissertation aims to enhance the stable operation and effective management of WWTPs by addressing these limitations through the pursuit of three objectives: (1) investigating an efficient data-driven approach for uncertainty analysis of process-driven secondary settling tank models; (2) developing data-driven models that can leverage sparse and imbalanced data for the prediction of emerging contaminant removal; (3) exploring an advanced data-driven model for influent flow rate predictions during the COVID-19 emergency. / Thesis / Doctor of Philosophy (PhD) / Ensuring appropriate treatment and recycling of wastewater is vital to sustain life. Wastewater treatment plants (WWTPs), which have complicated processes that include several intricate physical, chemical, and biological procedures, play a significant role in the water recycling. Due to stricter regulations and complex wastewater composition, the wastewater treatment system has become increasingly complex. Therefore, it is crucial to use simplified versions of the system, known as wastewater modeling, to effectively operate and manage the complex system. The aim of this thesis is to develop data-driven approaches for wastewater modeling.

Organische Schadstoffe werden in vielen Phasen unseres täglichen Lebens in den Wasserkreislauf eingeleitet. Die herkömmliche Abwasserbehandlung ist nicht zur effektiven Entfernung einiger dieser Stoffe, insbesondere von Arzneimitteln, geeignet. Die Fotokatalyse basierend auf der Suspension von katalytischen Nanopartikeln und ultraviolettem Licht stellt eine effiziente Methode dar, um diese organischen Stoffe im Abwasser zu reduzieren. Während das allgemeine Konzept der fotokatalytischen Wasserreinigung gut etabliert ist, fehlte ein beschreibendes und einfach anwendbares Modell der wesentlichen Abbauprozesse. Ein solches Modell ist entscheidend, um experimentelle Ergebnisse systematisch vergleichen zu können, und stellt eine wertvolle Hilfe bei der Optimierung von Prozessen dar. Diese Arbeit präsentiert einen Modellierungsansatz zur Simulation der kinetischen Prozesse basierend auf dem Langmuir-Hinshelwood-Mechanismus. Dieses Grundmodell wurde erweitert, um auch die Bildung von organischen Zwischenprodukten zu beschreiben. Diese Erweiterungen basieren entweder auf einem inkrementellen oder einen fragmentierenden Abbaumechanismus, der durch das Einbinden von überschüssigen Bindungen ergänzt werden kann. Die simulierte Konzentrationsentwicklung von Zwischenprodukten sowie die Entwicklung des verbleibenden organischen Kohlenstoffes werden für verschiedene Modellannahmen bezüglich ihrer Desorptionsraten von der Photokatalysatoroberfläche diskutiert. Die Modellparameter wurden aus dem Vergleich mit experimentellen Ergebnissen ermittelt. Grundlegende Experimente wurden unter Verwendung des Antibiotikums Ciprofloxacin und des Farbstoffs Methylenblau als Beispiele für organische Verbindungen und Titandioxid und Zinkoxid als fotokatalytische Materialien durchgeführt. Darüber hinaus wird die Anwendbarkeit des Modells auf komplexere Systeme durch den Vergleich mit dem fotokatalytischen Abbau von 14 Medikamenten im Abfluss von Kläranlagen demonstriert. Nach der Evaluierung des Modells wurde es in ein Open-Source-Softwarepaket implementiert, um eine breitere Anwendung zu ermöglichen und eine solide Grundlage für weitergehende Entwicklungen zu schaffen.:Abstract Kurzfassung Symbols Constants Abbreviations 1. Motivation 2. Introduction 2.1. Modeling and simulation 2.2. Heterogeneous photocatalysis 2.2.1. History 2.2.2. Semiconductor band structure 2.2.3. Interface between a semiconductor and a redox electrolyte 2.3. Photocatalytic material 2.3.1. Overview 2.3.2. Titanium dioxide 2.3.3. Zinc oxide 2.4. Light sources 2.4.1. Solar 2.4.2. Fluorescent tubes and mercury-vapor lamps 2.4.3. Light-emitting diodes 2.4.4. Organic light-emitting diodes 3. Materials and methods 3.1. Analytic methods 3.1.1. Nanoparticle characterization 3.1.2. Ultraviolet-visible absorption spectrometry 3.1.3. SPE-HPLC-MS/MS 3.1.4. Non-purgeable organic carbon 3.2. Experimental investigations 3.2.1. Model substances 3.2.2. Adsorption-desorption 3.2.3. Photocatalytic degradation 3.2.4. Wastewater treatment plant effluent 3.3. Modeling approach 3.3.1. Single organic species model 3.3.2. Multiple organic species model 3.4. Model implementation 3.4.1. Development objectives 3.4.2. Molecule parameters 3.4.3. Solving the differential equation system 3.4.4. Fit to experimental results 3.4.5. Availability 4. Results and discussion 4.1. Nanoparticle properties 4.2. Adsorption-desorption 4.3. Photocatalytic degradation 4.3.1. Single organic species model 4.3.2. Multi organic species model 4.4. Wastewater treatment plant effluent 4.4.1. Influence of effluent 4.4.2. Degradation of pharmaceuticals in the effluent 5. Conclusions Appendix A. Analytical solution B. Effluent pharmaceuticals concentrations C. pdom handbook List of figures List of tables Bibliography / Organic pollutants are discharged into the water cycle at many stages in our daily lives. Conventional wastewater treatments are ineffective in the removal of some of them, especially clearing pharmaceuticals. Photocatalytic degradation utilizing catalytic nanosuspensions under ultraviolet irradiation represents an efficient method to reduce those organic components in the wastewater. While the general concept of photocatalytic water purification is well established, a descriptive and easy to use model of the essential processes was missing. Such a model is critical to ensure the systematic comparability of experimental results and supports process optimization. This work presents a modeling approach to simulate the involved kinetic processes based on the Langmuir–Hinshelwood mechanism. Further, the fundamental model is extended to include the formation of intermediate organic components. This extension uses either an incremental degradation mechanism or a fragmentation based mechanism, that can include excess bonds. The simulated concentration evolution of intermediates, as well as the evolution of the total organic carbon, are discussed for different model assumptions concerning their desorption rates from the photocatalyst surface. The model parameters were estimated from comparison with experimental findings. Basic experiments were performed using the antibiotic ciprofloxacin, and the dye methylene blue as organic compounds and titanium dioxid and zinc oxide as photocatalytic materials. Furthermore, the application of the model to more complex systems is shown by the photocatalytic degradation of 14 pharmaceuticals in wastewater treatment plant effluent. Following successful evaluation of this model, it was implemented in an open-source software package to enable a wider adoption and a sound foundation for further developments.:Abstract Kurzfassung Symbols Constants Abbreviations 1. Motivation 2. Introduction 2.1. Modeling and simulation 2.2. Heterogeneous photocatalysis 2.2.1. History 2.2.2. Semiconductor band structure 2.2.3. Interface between a semiconductor and a redox electrolyte 2.3. Photocatalytic material 2.3.1. Overview 2.3.2. Titanium dioxide 2.3.3. Zinc oxide 2.4. Light sources 2.4.1. Solar 2.4.2. Fluorescent tubes and mercury-vapor lamps 2.4.3. Light-emitting diodes 2.4.4. Organic light-emitting diodes 3. Materials and methods 3.1. Analytic methods 3.1.1. Nanoparticle characterization 3.1.2. Ultraviolet-visible absorption spectrometry 3.1.3. SPE-HPLC-MS/MS 3.1.4. Non-purgeable organic carbon 3.2. Experimental investigations 3.2.1. Model substances 3.2.2. Adsorption-desorption 3.2.3. Photocatalytic degradation 3.2.4. Wastewater treatment plant effluent 3.3. Modeling approach 3.3.1. Single organic species model 3.3.2. Multiple organic species model 3.4. Model implementation 3.4.1. Development objectives 3.4.2. Molecule parameters 3.4.3. Solving the differential equation system 3.4.4. Fit to experimental results 3.4.5. Availability 4. Results and discussion 4.1. Nanoparticle properties 4.2. Adsorption-desorption 4.3. Photocatalytic degradation 4.3.1. Single organic species model 4.3.2. Multi organic species model 4.4. Wastewater treatment plant effluent 4.4.1. Influence of effluent 4.4.2. Degradation of pharmaceuticals in the effluent 5. Conclusions Appendix A. Analytical solution B. Effluent pharmaceuticals concentrations C. pdom handbook List of figures List of tables Bibliography