Biomineralization of phosphorus during anaerobic treatment of distillery wastewaters
Bibliographic record
Abstract
This study addresses the pressing environmental concerns associated with the rapidly growing distillery industry, which is a significant contributor to wastewater generation. By focusing on the treatment of distillery wastewater using anaerobic digestion, this research explores the potential to convert organic materials into biofuels (methane). Moreover, the study aims to recover both methane and phosphorus from distillery wastewater in a single anaerobic reactor, which represents a novel and unexplored approach. Laboratory-scale experiments were conducted using mesophilic and thermophilic upflow anaerobic sludge blanket reactors. A key aspect of the study involved the implementation of a unique strategy: the mixing of centrate and spent caustic wastewater streams. This approach was intended to enhance treatment performance, manipulate the microbial community structure, and thereby optimizing the overall treatment performance. The integration of the centrate and spent caustic streams yielded remarkable co-benefits, resulting in significant biomethane production and efficient phosphorus precipitation. The study demonstrated a phosphorus removal efficiency of ~60 % throughout the 130–140 days operation period. The recovery of phosphorus via the reactor sludge offers exciting opportunities for its utilization as a fertilizer or as a raw material within the phosphorus refinery industry. The biomethane produced during the treatment exhibits significant energy potential, estimated at 0.5 GJ/(m3 distillery wastewater).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".