iWISE: A Framework for Implementation of Intelligent Water Systems

Abstract

Aging infrastructure, along with the escalating occurrence and severity of extreme weather events linked to climate change, and the growing demands of an increasing population, have placed significant strains on wastewater and stormwater systems. Consequently, there has been a rise in instances of Combined and Sanitary Sewer Overflows (CSOs and SSOs), among other related problems. These challenges have intensified the impact of sewershed issues on both society and the environment. Fortunately, recent advancements in technology, such as sophisticated sensor technologies, more powerful processors, and advanced mathematical modeling techniques, have opened up new possibilities for developing intelligent water systems in the United States that are capable of making well-informed, data driven decisions. While the technological capabilities of these tools are advancing, their application in the water sector is limited and often siloed. Water utilities face a variety of challenges related to digitalization of sewershed management and require a more structured approach for their digital transformation. This research aims to present a comprehensive framework called iWISE (Intelligent Water Infrastructure Systems Engineering) that will serve as a blueprint to facilitate the implementation of Intelligent Sewersheds for water utilities across the country. The proposed framework will focus on enhancing our understanding of various aspects, including system of systems thinking, data management, modeling techniques, decision-making processes, and service delivery, in order to adopt a more intelligent and efficient approach to managing sewersheds. This framework was piloted with small, medium and large scale utilities to capture feedback on the proposed building blocks from a real world perspective and the findings from these interviews reveal that most utilities are in the preliminary stages of intelligent water systems implementation, and is more common among large utilities as compared to their small and medium counterparts.