AI-Powered Water Treatment: How to Capture Decades of Operator Expertise Before It Walks Out the Door

The specific requirements vary by application, but here’s a comprehensive starter list:

Core Operational Data:

• Flow rates (influent, effluent, and internal process flows)
• Chemical dosing rates and inventory levels
• Equipment runtime hours and cycling patterns
• Tank levels and detention times
• Pressure readings across the system

Treatment Performance Metrics:

• Influent and effluent quality parameters (BOD, TSS, nitrogen, phosphorus, pH, dissolved oxygen)
• Laboratory results with proper timestamps
• Permit compliance data and exceedance events

Equipment Status Information:

• On/off states for all major equipment
• Variable frequency drive (VFD) speeds and power consumption
• Maintenance schedules and work order history
• Equipment failures, repairs, and replacements

Environmental Context:

• Weather data (temperature, precipitation, humidity)
• Seasonal patterns and peak flow events
• Industrial discharge events and composition

Operational Context:

• Operator notes and shift logs
• Manual overrides and adjustments
• Emergency response actions and outcomes

The Hidden Data Challenge That Breaks AI

Your SCADA probably captures much of the list above. But here’s where things get complicated: the most valuable operational context often lives outside your data systems entirely.

Consider these real-world scenarios:

• Blower #7 was offline for five months for repairs, but you swapped in a temporary replacement with half the horsepower. Your SCADA logged continuous operation without knowing the equipment had completely changed.
• An experienced operator determined that opening the manual bypass valve by 10% prevented equipment from seizing during low-flow periods. Smart decision, but your SCADA has no record of this critical adjustment.
• During an upset condition, the team implemented a creative workaround involving three different process modifications. It worked brilliantly, but the details exist only in someone’s memory and a handwritten note.

This institutional knowledge—the troubleshooting hacks, equipment quirks, and creative solutions—represents the most valuable training data for AI. Yet it’s often completely invisible to data collection systems.

Building Your Foundation: What to Do Right Now

Whether AI deployment is next quarter or three years away, you can start preparing today:

1. Ensure Comprehensive Data Storage

• Verify your SCADA is capturing and storing all available data points
• Implement cloud-based or offsite SCADA historians for long-term retention
• Establish data backup protocols to prevent loss

2. Document Manual Interventions

• Create processes in your work order system to log manual equipment adjustments
• Record valve positions, bypass operations, and non-automated changes
• Timestamp all manual interventions to correlate with SCADA data

3. Capture Institutional Knowledge

• Document emergency response procedures with specific actions and outcomes
• Record equipment-specific quirks and operational workarounds
• Create structured formats for operator notes that can be digitized later
• Conduct knowledge transfer sessions with senior operators before retirement

4. Clean and Contextualize Historical Data

• Identify and correct data gaps or anomalies in your archives
• Add metadata explaining unusual operating periods
• Tag equipment swaps, major maintenance, and process modifications

Ready to Deploy? Ask These Critical Questions

If your data foundation is solid and you’re ready to explore AI implementation, start with strategic clarity:

Define Your Objectives:

• Are you primarily seeking improved automation and equipment efficiency?
• Do you want AI-powered operator training and decision support?
• Is 24/7 monitoring and early warning your top priority?
• What does success actually look like? Faster response times? Better permit compliance? Reduced chemical costs?

Establish Ownership:

• Who will be responsible for the AI platform—Operations, Engineering, or IT?
• What’s your governance structure for AI-generated recommendations?
• How will you handle situations where AI advice conflicts with operator judgment?

Choose Your Implementation Path:

• Will you develop custom AI solutions in-house with your specific operational knowledge?
• Do you prefer commercial off-the-shelf platforms with proven track records?
• What’s your risk tolerance for early adoption versus waiting for mature solutions?

Plan Your Integration:

• How will AI recommendations be delivered to operators—dashboards, mobile alerts, or SCADA integration?
• What training will your team need to work effectively with AI assistance?
• How will you measure and validate AI performance over time?

The Future of Water Treatment Operations

Regardless of where your team is on their digital journey, AI is here to stay and hopefully help your team. Pass this onto your lead operator or engineer so they can start planning for the future as well. 

Share this article with your lead operators, plant engineers, and facility managers. The decisions you make today about data collection and knowledge management will determine whether your utility leads the AI revolution or struggles to catch up.