AI Takes on Tides at Tampa Desal Plant

Tampa Bay Water tests AI forecasting to spot water quality threats before they disrupt operations

11 Feb 2026

Tampa Bay Water is piloting an artificial intelligence-based monitoring system at its Tampa Bay Seawater Desalination Plant, in a move that signals a shift in how US utilities manage operational risk.

The project, developed with infrastructure group ACCIONA and AI specialist SkyTL, aims to strengthen early warning capabilities in water quality management. It reflects growing interest in predictive tools as utilities face ageing infrastructure, budget pressure and increasing climate variability.

According to project partners and regional industry reports, the system is designed to identify potential water quality threats before they reach treatment operations. Instead of relying only on conventional intake sensors, the pilot combines satellite imagery, drone observations, fixed cameras and real-time environmental data.

Artificial intelligence models analyse these inputs to detect patterns that may indicate algae blooms, changes in salinity or spikes in turbidity, a measure of water clarity. The objective is to provide plant operators with earlier awareness of developing conditions.

Partners involved in the trial say the system may generate alerts up to two hours before disruptions affect plant processes. Even limited advance notice could allow operators to adjust treatment protocols in advance, potentially lowering chemical use and reducing the need for reactive interventions.

The initiative also highlights a broader pattern of collaboration between public utilities and private technology providers. For companies such as ACCIONA, pilot projects of this kind form part of a wider strategy to expand predictive intelligence tools across critical infrastructure sectors.

However, the programme remains a pilot at a single facility rather than an established industry model. Smaller utilities may face financial and technical barriers to adopting similar systems, and regulators are likely to require evidence of reliability before AI-based forecasts are used in compliance decisions.

As climate pressures intensify and operational demands increase, predictive monitoring is drawing attention as a complement to traditional safeguards. Wider adoption will depend on demonstrable results, regulatory approval and sustained investment.