Smart Water Management Using Intelligent Digital Twins

Abstract

Providing and distributing fresh water to large communities is a major global concern. In addition to its scarcity as well as to its wastage, this vital resource is being affected by challenging environmental conditions. New approaches are, therefore, urgently needed for an optimized, fair, and efficient use of fresh water. The adoption of emergent technologies is giving high hopes to reach this objective. Among these technologies, digital twin is attracting increasing attention from the academic and industrial committees. This attention is particularly motivated by its expected values to any sector, including process optimization, cost reduction, and time to market shortening. In the specific field of water management, several solutions are being proposed, especially to detect leak detection and test water assets under a variety of working constraints. These solutions are still lacking intelligence and autonomy throughout the loop of data acquisition and processing as well as asset control and service generation and delivery. To this end, we are proposing in this paper a new framework based on multi-agent systems and DT paradigm to close gaps within this loop. Our multi-agent system is responsible of running data analytics mechanisms in order to assess water consumption and generate relevant feedbacks to users using, among others, a rewarding system to select the appropriate pricing policies. It is also responsible of simulating asset operations under specific working constraints for the purpose of failure and/or defect detection.