Efficient Water Management through Intelligent Digital Twins

Purpose: Supplying and distributing fresh water to large populations is a significant global issue. In addition to the challenges posed by its scarcity and wastage, this essential resource is increasingly vulnerable due to adverse environmental conditions. Consequently, there is an urgent need for novel approaches to ensure the optimal, equitable, and efficient utilization of fresh water. The emergence of new technologies offers promising prospects for achieving this goal. One such technology, the digital twin, is gaining considerable attention from both academic and industrial communities. This attention is primarily driven by the anticipated benefits it offers across various sectors, including process optimization, cost reduction, and accelerated time to market. Methodology: In the realm of water management, numerous solutions are being proposed, particularly aimed at detecting leaks and assessing water assets under diverse operational conditions. However, these solutions often lack sufficient intelligence and autonomy throughout the entire data acquisition and processing cycle, as well as in asset control and service provision. To address this gap, we propose a new framework in this paper, based on multi-agent systems and the digital twin paradigm. Findings: Our multi-agent system is tasked with conducting data analytics to evaluate water consumption and delivering relevant feedback to users. This includes implementing a rewarding system to incentivize appropriate pricing policies. Additionally, the system simulates asset operations under specific constraints to facilitate the detection of failures or defects. Unique Contribution to theory, practice and policy: We propose employing Markov Decision Process (MDP), a mathematical framework for decision-making, to model water consumption behaviors.