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A PROACTIVE APPROACH TO MANAGING BIOSOLIDS: USING STATISTICAL MODELING TO OPTIMIZE COST, PRODUCT QUALITY, AND PUBLIC RELATIONS

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Wastewater treatment plants perform an important social function in recycling wastewater solids. Beneficial reuse of these wastes through land application occurs on farms, forests, tree farms, and mine reclamation sites. Additionally, these wastes can be disposed of in landfills and incinerators however these options are less directly helpful to society. Despite the fact that land application of biosolids in beneficial reuse projects is an accepted practice and is in fact the backbone of many biosolids programs in North America, this process is still scrutinized when nuisance odors occur. The wastewater industry has usually taken a reactive approach to biosolids odors, reacting to complaints in the field and doing damage control for complaints received. Research in recent years has led to findings showing that several process parameters at the wastewater treatment plant can have a dramatic affect on the odor quality of the biosolids product. In this paper we present several statistical models that predict biosolids odor levels based on processing and management variables as well as ambient conditions. These models offer biosolids managers a tool to act proactively and predict when odorous materials will be produced at the plant. This information, combined with GIS data for receiving areas offers an opportunity to ensure that biosolids of a certain odor profile are matched with a site suitable to accept the odors without adversely impacting the neighboring communities. We illustrate the usefulness of these models via several realistic scenarios that simulate what the odor impact might be. Results of this study include calibrated models using existing process data and field odor data collected by inspectors for one year. Additionally, this study shows how small changes in process parameters can show dramatic changes in odor quality, and that two small changes, while individually may show minor changes in odor, when combined together can have a much larger effect. The model is run for three scenarios that combine different process changes and compares results to the results of the model using average process data.
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Document Type: Research Article

Publication date: 01 January 2004

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