How much data is required for a robust and reliable wastewater characterization?

Abstract

Abstract Water resource recovery facility (WRRF) modeling requires robust and reliable characterization of the wastewater to be treated. Poor characterization can lead to unreliable model predictions, which can have significant economic consequences when models are used to make important facility upgrade/expansion and operational decisions. Current wastewater characterization practice often involves a limited number of relatively short-duration intensive campaigns. On-going work at the Great Lakes Water Authority (GLWA) WRRF, serving 3.1 million residents in Southeast Michigan, provided an opportunity to conduct more detailed wastewater characterization over an annual cycle. The collection system includes a significant combined sewer component, and the WRRF provides primary and secondary treatment (high purity oxygen activated sludge) and phosphorus removal via ferric chloride addition. Detailed wastewater fractionation was conducted weekly over a one-year period. Daily conventional secondary influent and process operational data from that same period were used to evaluate the efficiency of various wastewater characterization strategies on the bioreactor mixed liquor volatile suspended solids (MLVSS) concentration calculated using an International Water Association (IWA) Activated Sludge Model Number 1 (ASM1) with minor modifications. An adaptive strategy consisting of a series of short-duration characterization campaigns, used to assess model fit for its intended purpose and continued until a robust and reliable model result, is recommended. Periods of unusual plant influent and/or operational conditions should be identified, and data from these periods potentially excluded from the analysis. Sufficient data should also be collected to identify periods when poor model structure, rather than wastewater characterization, leads to poor fit of the model to actual data.