Production monitoring of water/wastewater

A water, sewer and electric utility provider in Florida has develped an automated software system for supply and distribution that substantially improves water operations in the age of high-priced power.

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Optimize This!
The optimization strategy minimizes energy consumption during high-energy cost hours. The output of the model is a production schedule for each plant. The expert system specifies the combinations of well pumps to run at each hour and the order for running pumps across a 24-hour period for each well. This schedule is assessed each day and can be altered to meet changing conditions or constraints. Detailed pump operating schedules are transmitted directly to PLCs that run the well-field and high-service pumps. The supply optimizer generates stable operating schedules, reducing pumping during peak hours according to the cost-profile function, while maintaining the reservoir levels within the user-defined minimum and maximum values.

G2 makes managing an optimizer for closed-loop control practical by enabling work at a higher level of abstraction. JEA engineers defined inputs, such as sensors, and outputs, such as equipment set points, as software objects. They represented the rules-based logic for applying the optimizer calculations in an intuitive natural-language script that incorporates the concept of time. Typical optimization tasks, such as averaging the reading of a meter over time, checking a meter reading against mass balances, or checking the position of a pressure relief valve before changing a set point, were made parametrically without having to recode the algorithms.

Changes on the Fly
The expert system includes a user interface so operators can make adjustments to constraints and reporting. For example, operators can compare the projections against the actual reservoir levels. This same information is used by the expert system to adapt to changing conditions automatically. When the difference between the projected and actual reservoir level exceeds magnitude and rate-of-change criteria, a new set of constraints is captured, and a new schedule generated. This combination of feed-forward and feedback capabilities produces a highly adaptive control that makes the best use of forecasts and real-time events.

Well-field optimization was put into service less than eight weeks from project conception. The first sub-grid of the distribution system was ready for on-site testing and calibration six months after project conception.

The results in this sub-grid demonstrate that optimization makes better use of reservoir capacity by drafting, or lowering, levels as needed to minimize cost and maximize well quality. Well-field conductivity has been improved substantially, providing an indication that salt intrusion has been reduced, which will significantly improve water quality. Some wells have even shown a downward trend in conductivity, indicating that the health of these wells is increasing.

This application, when completed for JEA’s network of over 39 facilities, will enhance the company’s ability to service its growing customer base, reduce the need to invest in new wells and water mains, lower pumping energy costs, reduce equipment failures and minimize maintenance.


  About the Authors
Michael Eaton is a Technology Specialist at JEA, and Darren Hollifield is the Water Treatment Operations and Maintenance manager.
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