A novel approach for harnessing biofilm communities in moving bed biofilm reactors for industrial wastewater treatment
Bibliographic record
Abstract
Moving bed biofilm reactors (MBBRs) are an effective biotechnology for treating industrial wastewater. Biomass retention on moving bed biofilm reactor (MBBR) carriers (biofilm support materials), allows for the ease-of-operation and high treatment capacity of MBBR systems. Optimization of MBBR systems has largely focused on aspects of carrier design, while little attention has been paid to enhancing strategies for harnessing microbial biomass. Previously, our research group demonstrated that mixed-species biofilms can be harvested from an industrial wastewater inoculum [oil sands process water (OSPW)] using the Calgary Biofilm Device (CBD). Moreover, the resultant biofilm communities had the capacity to degrade organic toxins (naphthenic acids—NAs) that are found in OSPW. Therefore, we hypothesized that harnessing microbial communities from industrial wastewater, as biofilms, on MBBR carriers may be an effective method to bioremediate industrial wastewater.<BR>Here, we detail our methodology adapting the workflow employed for using the CBD, to generate inoculant carriers to seed an MBBR.<BR>In this study, OSPW-derived biofilm communities were successfully grown, and their efficacy evaluated, on commercially available MBBR carriers affixed within a modified CBD system. The resultant biofilms demonstrated the capacity to transfer biomass to recipient carriers within a scaled MBBR. Moreover, MBBR systems inoculated in this manner were fully active 2 days post-inoculation, and readily degraded a select population of NAs. Together, these findings suggest that harnessing microbial communities on carriers affixed within a modified CBD system may represent a facile and rapid method for obtaining functional inoculants for use in wastewater MBBR treatment systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".