An industrial client faced challenges posed by selenium leaching into groundwater and surface water. The company had selected a biological system that relies on microbes to remove dissolved selenium from wastewater; the process generates biosolids containing residual selenium.
Our client’s goal was to assess correlations between microbial-ecology and system-operations data to learn whether modifying the microbes’ environment had the potential to boost treatment system effectiveness. Before committing significant amounts of money to make operational changes, though, the company asked Barr to perform a preliminary statistical analysis.
There was little information in wastewater or academic literature about facilities using microbial-ecology data to directly inform treatment operations, especially for selenium-removal processes. Data the client had collected for the project included 16S ribosomal RNA (rRNA) community profiles, adenosine triphosphate (ATP) measurements, and microscopy data.
Barr’s analysis of that data correlated several dominant microbial groups with plant performance. When we evaluated those groups further to determine which system conditions would promote or inhibit microbial activity, the operational parameters predicted to limit concentrations of reduced selenium in the effluent were typically the same as those we identified through direct pairwise correlation of the parameters and reduced-selenium concentrations. In other words, the direct application of microbial-ecology data to inform plant operation did not yield much promise for improving treatment effectiveness—a finding that gave our client the insight needed to avoid spending money on further analyses.
Still, evaluating the microbiology data provided the company with several benefits, including measuring microbial activity and performance in real time via ATP and microscopy techniques, and gaining a microbial-community operational baseline to support future root-cause analyses of reactor upsets.