Biological filtration for membrane pre-treatment and other applications: towards the development of a practically-oriented performance parameter
Bibliographic record
Abstract
Research Article| June 01 2008 Biological filtration for membrane pre-treatment and other applications: towards the development of a practically-oriented performance parameter P. M. Huck; P. M. Huck 1University of Waterloo, Waterloo, Ontario, Canada Tel.: 1-519-888-4567 ext.32707; E-mail: pm2huck@uwaterloo.ca Search for other works by this author on: This Site PubMed Google Scholar M. M. Sozan´ski M. M. Sozan´ski 2Technical University of Poznan, Poznan, Poland Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2008) 57 (4): 203–224. https://doi.org/10.2166/aqua.2008.010 Article history Received: May 15 2007 Accepted: October 12 2007 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation P. M. Huck, M. M. Sozan´ski; Biological filtration for membrane pre-treatment and other applications: towards the development of a practically-oriented performance parameter. Journal of Water Supply: Research and Technology-Aqua 1 June 2008; 57 (4): 203–224. doi: https://doi.org/10.2166/aqua.2008.010 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Biological processes have a long history of use in drinking water treatment. In recent decades, rapid biological filtration has achieved increasing acceptance. In terms of organic carbon, the classical application of biofiltration has been for the removal of easily biodegradable material, along with the related objectives of reduction in disinfectant demand and byproduct formation. Additional objectives receiving emphasis more recently include the removal of trace contaminants and the use of biofiltration as a membrane pre-treatment to reduce fouling. Although various models for biofiltration have been developed, these are generally complicated and research-oriented, and no modeling approach or performance parameter has found general acceptance in practice.The paper summarizes previous modeling approaches and illustrates the usefulness of a previously developed index of contact time, X*, referred to as dimensionless contact time. In addition to empty bed contact time, X* contains other factors which are important for biofiltration performance: the reactor surface area available for biofilm development and substrate biodegradability and diffusivity. This paper shows how X* can be simplified and also illustrates its application to the removal of trace contaminants (e.g. pharmaceuticals, endocrine disrupting substances or odorous compounds such as geosmin). A major section of the paper analyzes the requirements for biofiltration when used as a membrane pre-treatment to reduce fouling, and illustrates the applicability of X* for this use of biofiltration. Thus X* is presented as an overall unifying parameter. The last section of the paper introduces a new practically-oriented parameter, the Biofiltration Factor (BF). The use of this parameter, which is directly related to X*, is proposed for biofiltration design and operation, once some additional work has been undertaken. biofiltration, dissolved bacterial product material, drinking water, geosmin and MIB, membrane fouling, trace contaminants This content is only available as a PDF. © IWA Publishing 2008 You do not currently have access to this content.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".