Why Influent Monitoring Matters for Design and Operations

Nick Landes Ph.D.

Presented at the GAWP Virtual Fall 2020 Conference Series.

Raw wastewater influent is a critical factor governing the design and daily performance of water resource recovery facilities. Despite its criticality for design and operations, influent flow and water quality monitoring often receive little attention, or in some cases, are completely neglected.

Influent monitoring is important for a host of reasons. From an operator’s perspective, influent monitoring can be used to guide operational decisions such as shutting down tanks for maintenance and cleaning procedures; adjusting mixed liquor solids inventory based on the food to mass ratio; flow pacing return activated sludge recycle rates or chemical dosing rates; identifying atypical load increases caused by industrial or septic tank hauler discharges; and detecting the presence of toxic or inhibitory compounds. From a designer’s perspective, influent monitoring is critical for improving the validity of flow and load forecasts; right-sizing unit processes; and calculating critical factors affecting operational costs such as aeration demands, solids production rates and chemical dosing requirements. Both operations and design benefit from improved understanding of how flows and loads change based on diurnal, weekly (weekday vs weekend), seasonal and long-term trends. Although the list above is not encyclopedic, it provides a general basis for the importance of influent monitoring.

This presentation discusses the importance of influent sampling from the perspective of both utilities and design engineers. The presenters discuss critical influent flow and water quality sampling factors to consider including sampling location, analytes to measure, and the purpose and application of sampling data for operational and design performance. The importance of influent sampling will be underscored by comparing process and design implications with and without good influent flow and water quality data. These example scenarios and case studies include comparing operating costs for adjusting versus not adjusting to seasonal influent changes, comparing the capital and operating costs for a facility designed using textbook assumptions versus actual influent water quality data, and identification of process upsets caused by internal return streams.

 

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