Hybrid Models in Wastewater: Merging Mechanistic with Data-Driven

Description

Recent literature shows examples of modelling approaches for integrated urban wastewater system (UWS) operation and asset management. Such examples range from classical theory-based approaches, using mechanistic or empirical knowledge, to theory-free data-driven models using big data.

On one hand, mechanistic models have been extensively used for process design and optimization. However, building the perfect mechanistic model is only possible for the simplest systems, consequently not for biological processes. The resulting limited prediction capability is a limiting factor in using them for real-time decision-making. On the other hand, “pure” big data models exist but remain isolated initiatives in the academic sector.

The close integration of mechanistic models with Big Data processing and learning techniques has the potential to transform how the day-to-day integrated operation of UWS is currently being done.

In this webinar, we will showcase the potential of hybridized mechanistic and data-driven models in the wastewater sector by presenting three existing initiatives that are working towards integrating both paradigms into hybrid models.

Participants are expected to have basic knowledge of: water and wastewater treatment fundamentals and process modelling, data collection and reconciliation in the water sector, and data science.