Physics-informed and data-driven modeling of an industrial wastewater treatment plant with actual validation

• Published in: Computers & chemical engineering 2024-10, Vol.189, p.108801, Article 108801
• Main Authors: Koksal, Ece Serenat, Asrav, Tuse, Esenboga, Elif Ecem, Cosgun, Ahmet, Kusoglu, Gizem, Aydin, Erdal
• Format: Article
• Language: English
• Subjects: Chemical oxygen demand; Data-driven modeling; Dissolved oxygen concentration; Physics-informed neural networks; Wastewater treatment
• ISSN: 0098-1354
• DOI: 10.1016/j.compchemeng.2024.108801

•Physics-informed modeling to predict parameters in a real activated sludge system.•Online validation step for physics-informed and data driven models.•Importance of physics-informed modeling when standard modeling falls short.•Physics-informed modeling may be unnecessary when standard modeling suffices.•Physics-informed modeling without the requirement for fine-tuning. Data-driven modeling is essential in chemical engineering, especially in complex systems like wastewater treatment plants. Recurrent neural networks are effective for modeling parameters in wastewater treatment process such as dissolved oxygen concentration and chemical oxygen demand due to their nonlinear adaptability. However, traditional models face challenges such as the requirement for larger datasets and more frequent sampling, noisy measurements, and overfitting. To address this, physics-informed neural networks integrate physical knowledge for improved performance. In our study, we apply both approaches to a wastewater treatment plant, enhancing prediction performance. Our results demonstrate that physics-informed models perform successfully in offline and online validation, especially when standard methods fail. They maintain effectiveness without frequent updates. Yet, integrating physics-informed knowledge can introduce noise when standard methods suffice. This result points out the need for careful consideration of model choice in different scenarios.