Implementation of Sensors to Support Enhanced Nutrient Removal
- Phill Yi, Wendell Khunjar, Katya Bilyk, Ron Latimer, Paul Pitt - Hazen and Sawyer
- Charles Bott - HRSD
- Maureen O’Shaughnessy - PWCSA
Analytical devices (sensors) comprise an integral component of wastewater treatment systems as they can allow automation of data collection, thereby freeing up resources for other tasks and allowing these data to be used for both manual and automatic control of facilities and processes. Use of sensors to meet stringent nutrient discharge limits is of high interest. Despite fairly widespread deployment for nutrient removal, little guidance exists to help utilities apply sensors to solve specific nutrient removal challenges. Better understanding of instrumentation equipment, control strategies, installation design and on-going maintenance requirements will benefit the speed and success of implementation.
This paper will provide a summary of experiences from the Mid-Atlantic region to address these limitations. A survey was conducted at seven different wastewater treatment plants (WWTPs) operated by four different utilities throughout the state of Virginia as well as one pilot study. Treatment plants ranged in size and process configuration. The survey consisted of an interview with the plant managers, instrumentation technicians and/or operators involved with the daily operation and maintenance of the sensors. During the interview, 27 different questions were addressed which aimed at identifying both quantitative and qualitative data relative to sensor maintenance, calibration, validation, and use for facility operation. Collected data were analyzed and compiled in order to develop specific case studies. It is anticipated that through these case studies challenges and barriers that utilities faced when first considering sensors will be identified, as well as the experiences and strategies of utilities that successfully addressed these issues. Case studies describing emerging applications for sensors (e.g., online process control and predictive modeling) will also be included to the extent possible.
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