Unplanned equipment failures drive up operational costs, increase downtime, and strain critical resources in water and utility networks. Predictive asset management offers a way forward using models not only to detect emerging faults, but also to optimize maintenance schedules, extend asset lifetimes, and reduce energy and carbon costs.
In this webinar, we will demonstrate how to design and deploy a digital twin of a water treatment plant using Dyad, JuliaHub’s system simulation environment. By integrating Kalman filter–based predictive health monitoring, the model will anticipate component degradation and identify optimal interventions before failures occur. We will then show how to scale this capability by deploying the solution to the cloud via JuliaHub’s application deployment tools, enabling secure, on-demand access for operations and engineering teams.
Attendees will gain insight into how Dyad and JuliaHub together enable:
Predictive maintenance through model-based state estimation and fault detection.
Resource optimization by linking asset health predictions to energy efficiency and operational planning.
Seamless transition from model development to enterprise deployment.
