Filtration of zinc ions utilising pretreated <i>Streptomyces rimosus</i> biomass
Bibliographic record
Abstract
Abstract The performance of biofiltration of zinc utilising pretreated Streptomyces rimosus was studied. Streptomyces rimosus biomass is able to bind zinc ions in batch mode. The biomass granules may be regenerated easily by using a biomass pretreatment which confers rigidity to biosolids, without decreasing the zinc uptake capacity, thus allowing collection of the biomass by filtration. Accordingly, biomass was pretreated with an anionic enzymatic tension active product (Extran AP41) and regeneration with a cleaning product (HCl) was successfully realised. It was shown that the optimum concentration of biomass and pressure range are found to be between 50 and 120 g dm−3 and 0.5 and 1 × 105 Pa, respectively. Complete regeneration was reached after three cycles under optimal experimental conditions when the biosorbent was saturated with synthetic ZnCl2 solution. The filterability of biosolids was demonstrated. A combination of a batch reactor and a filtration process made it possible to increase the performance of the complete treatment process. The biosorption capacity of the biomass to bind Zn ions was slightly increased (from X = 14 mg g−1 in batch mode to X = 16.1 mg g−1 in a process combining batch reactor and pressure filtration) and the experimental contact time was considerably reduced. Integration of the filtration process produced a dewatering cake which considerably facilitated the regeneration operation. Copyright © 2003 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".