One of the most common pitfalls in pilot testing is a lack of clarity around why the pilot is being conducted. Before any equipment is mobilized, stakeholders should align on the primary objectives.

Ask questions such as:

• What decisions will be made based on the pilot results?
• Are we validating treatment performance, operability, cost assumptions, or regulatory compliance?
• What parameters determine success or failure?

Establishing quantifiable success criteria—such as effluent quality targets, recovery rates, chemical usage, or fouling rates—ensures the pilot generates actionable conclusions rather than ambiguous results.

A pilot must realistically represent how the technology will operate at full scale.

This includes:

• Treating representative full-scale steady state source water (not synthetic or pre treated samples)
• Operating at representative hydraulic loading rates
• Using expected chemical types and dosages
• Planning for power, mechanical, and heat infrastructure as required for consistent, high-uptime availability
• Mimicking seasonal water quality variations when possible

If the pilot is run under “ideal” or overly controlled conditions, its results may not translate well once exposed to real world variability. When full replication isn’t feasible, assumptions should be explicitly documented.

Pilot duration matters. Short pilots may be sufficient for basic treatment performance validation, but longer-term operation is often required to understand:

• Long term fouling or scaling behavior
• Membrane media performance change over time
• Cleaning frequency and effectiveness
• Operator interaction and maintenance burden

The pilot timeline should reflect the risks being investigated, not just budget or schedule pressure. In many cases, several months of operation provide far more insight than a few weeks of data. This is particularly important for source waters that have variable water quality. For example, seasonal changes or changes in industrial operations.

Operators are often the ultimate users of the technology, but may not have availability to conduct a pilot program on top of their day-to-day. Operations staff involvement provides several benefits:

• Practical feedback on operability and maintenance
• Insights into training needs
• Early buy in for future implementation
• Identification of safety or procedural gaps
• Cost savings that scale based on remoteness from other stakeholder’s offices

Technologies that perform well but are difficult to operate or maintain may struggle at full scale.

Data collection is the core value of a pilot. Ensure that:

• Sensors are properly selected, calibrated, and maintained
• Data collection frequency is sufficient to observe trends and upsets
• Manual sampling protocols are clear and repeatable
• Data ownership and reporting responsibilities are defined

Equally important is determining how data will be analyzed and reported. A pilot that generates large volumes of data but lacks structured analysis can still leave decision makers uncertain.

Every pilot has limitations— a successful pilot program clearly documents these limitations so they are considered during interpretation.

For example:

• A pilot membrane system may not replicate full scale hydraulics
• Redundancy and automation may differ from final designs
• Operator availability during pilots may differ than at full scale

Acknowledging these realities ensures conclusions are applied appropriately and conservatively.

Pilots are often linked—directly or indirectly—to performance expectations, capital investment decisions, and considerations to mitigate risk.

It is important to define:

• How pilot results will influence final design and equipment selection
• How to manage the identified risk
• Back-up plans for inconclusive results

When suppliers and owners share a common understanding of how pilot outcomes affect commercial decisions, the process becomes more collaborative and transparent.

A pilot should never simply “end.” A formal close out phase ensures value is fully captured, including:

• Final performance summary
• Lessons learned and operational insights
• Updated assumptions for full scale design
• Clear recommendations and next steps if the results have uncovered new objectives