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Aigues Ter Llobregat (ATLL)

AIGUES TER LLOBREGAT (ATLL) Implements Predictive Water Pump Monitoring Across Regional Water Network

The Challenge

Aigues Ter Llobregat (ATLL) is a regional water utility responsible for delivering drinking water to more than 100 municipalities across Catalonia, Spain, drawing supply from both the Ter and Llobregat river systems. The utility operates a large, geographically dispersed automation infrastructure spanning multiple treatment plants and pumping stations — each a potential point of failure. Water supply pumps are critical assets: an unplanned outage threatens public health obligations that cannot be deferred. Without real-time condition visibility into pump health, the organization operated reactively — dispatching maintenance teams after failures rather than ahead of them. This approach exposed the utility to supply disruptions, emergency repair costs, and compliance risk under ISO9001 and EMAS environmental management standards.

The Solution

ATLL partnered with Rockwell Automation to deploy FactoryTalk AssetCentre within an Integrated Architecture framework, establishing centralized asset management across its distributed network of pump stations. The core of the implementation is an online predictive monitoring system that continuously collects operational data from water supply pumps — including flow, pressure, and mechanical performance parameters — and feeds it directly into the plant control system. Predictive ML models analyze this real-time data stream to detect early anomalies in pump behavior before they escalate into failures. Rather than running as a standalone tool, the condition monitoring capability was embedded within the existing control infrastructure, so operators receive actionable alerts within familiar interfaces. The deployment simultaneously established unified access control, automated backup, security policies, and asset auditing across all geographically dispersed sites.

Results

The implementation shifted ATLL's maintenance posture from reactive to predictive across a service network spanning more than 100 municipalities — a meaningful operational change for a utility with continuous supply obligations. Key outcomes include:

  • Integrated pump health visibility: Real-time condition data consolidated into the plant control system, eliminating the need for separate monitoring tools across dispersed stations
  • Proactive intervention capability: Early anomaly detection enables scheduled maintenance before pump failures disrupt water supply
  • Compliance infrastructure: Centralized access control, audit trails, and automated backup established across the network, directly supporting ISO9001 quality management and EMAS environmental certification requirements
  • Consistent security governance: A unified security framework applied across all distributed automation assets, reducing exposure from inconsistent site-level configurations

Key Takeaways

  • Dispersed pump networks benefit most when condition monitoring is integrated directly into the plant control system — standalone tools create operator blind spots and adoption friction
  • For compliance-driven utilities, access control and asset auditing should be treated as core deployment requirements, not optional add-ons
  • Geographically distributed infrastructure requires a centralized asset management platform that enforces consistent security and governance policies across all sites
  • Embedding predictive alerts within existing operator interfaces accelerates adoption compared to separate dashboards that compete for attention
  • Utilities serving large municipal areas should account for multi-site rollout complexity early — architecture decisions made at the first site set the pattern for the entire network

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Details

AI Technology
Predictive ML
Company Size
Enterprise
Quality
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