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A MONTE CARLO BASED RISK ASSESSMENT MODEL FOR DEVELOPING A SUSTAINABLE FLOW BLENDING STRATEGY AT A WASTEWATER TREATMENT PLANT

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Abstract:

Flow blending at treatment plants during periods of heavy rain has become a hot topic with a recent USEPA draft flow blending policy. This draft policy would allow treatment plants to combine wastewater which has received primary treatment with effluent that has received full secondary treatment. The major caveat is that final plant effluent cannot exceed the regular permit limits. This ability to flow blend has made permit compliance during rain storms a more attainable goal for many plants, but it has left many professionals scratching their heads, asking “What is the risk with flow blending?”

In order to examine how high rate treatment units, such as DensaDeg® or ACTIFLO®, would help permit compliance during storm events, A risk assessment model was developed for evaluating flow blending at the Southerly Waste Water Treatment Plant (SWWTP) in Columbus, OH, a city with over a million people in its service area. The model uses Monte Carlo sampling, popular in business applications, to account for variation of characteristic water quality parameters in the wastewater influent and effluent. The Monte Carlo method randomly samples a probability distribution developed from historic data to create a manageable data set with a probabilistic variance. The data set can then be modeled to predict final values of the characteristic parameters in the system. The end results of the model are probability distributions that show potential outcomes due to changes in the influent characteristics caused by rain events.

Document Type: Research Article

DOI: http://dx.doi.org/10.2175/193864705783867693

Publication date: January 1, 2005

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  • Proceedings of the Water Environment Federation is an archive of papers published in the proceedings of the annual Water Environment Federation® Technical Exhibition and Conference (WEFTEC® ) and specialty conferences held since the year 2000. These proceedings are not peer reviewed.

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