Author(s): Cesar Arenas-Prado; Sofia Jaray-Valdehierro; B Elduayen-Echave; Tamara Fernandez-Arevalo; Aitor Domec; Itxaro Errandonea; Saioa Arrizabalaga; E. Ayesa; Luis V. Valcarcel
Linked Author(s):
Keywords: Digital transformation; Digital twin; Energy efficient control; Multi-objective optimization; Decision support system; Real-time prediction
Abstract: Water Resource Recovery Facilities (WRRFs) have traditionally been modeled with mechanistic frameworks that use systems of differential equations to represent biochemical, chemical or physico-chemical transformations [1]. As these frameworks have matured to reflect evolving operations, their growing complexity has increased. In practice, models are often deployed offline for scenario analysis rather than continuous decision-making [2]. This has motivated the rise of data-driven approaches and hybrid strategies that couple mechanistic structures with machine-learning components to balance interpretability and adaptability [3]. Within DARROW European Project (https: //www. wastewater. ai/), we present a lightweight, flexible framework that delivers real-time predictions of key effluent variables and provides operator-friendly optimization under regulatory and operational constraints running fully online with minimal infrastructure.
Year: 2026