Sonny’s car-wash operations depend on a connected water system that moves water through municipal supply, reverse-osmosis treatment, product and reject streams, storage, reclaim treatment, and the wash tunnel. Tracking its performance requires more than individual sensor feeds and static charts. Sonny’s needed to see how field measurements related to the physical process, how conditions in one part of the system affected another, and what those relationships meant for day-to-day operations. This broader view also needed to support water- and mass-balance calculations and help identify issues across the system.
Barr combined engineering, instrumentation and controls, and digital technology to develop a real-time digital twin of Sonny’s water system. Instrumentation measuring flow, pressure, tank levels, conductivity, and dissolved oxygen was connected at key points across the municipal supply, reverse-osmosis, storage, reclaim, and wash processes. These measurements were integrated with the engineering calculations, data architecture, validation workflows, and dashboards needed to evaluate system performance. The resulting platform calculates indicators such as reverse-osmosis recovery, process-water demand, reclaim-system performance, and overall water balances, while allowing operations and technical teams to view current conditions, explore trends, evaluate data quality, receive alerts, and understand how tanks, treatment systems, water use, and individual measurements affect one another.
An agentic AI interface serves as a command center for the digital twin. Users can ask questions in natural language, investigate conditions, assess potential issues and data gaps, and generate custom interactive analyses grounded in approved measurements and engineering calculations. The agent can also update Grafana dashboards and create new visualizations as operational needs change, allowing teams to move from a question or emerging issue to a focused analysis without relying solely on predefined dashboards. By bringing data exploration, visualization, and engineering context into a single interface, the agent helps users determine what is happening, why it may be occurring, and where further attention is needed.
Together, the digital twin and its agentic AI command center give Sonny’s a stronger foundation for managing water use, evaluating treatment performance, investigating unusual conditions, and turning operational data into actionable improvements as the system evolves.
