Bibliographic record
Abstract
A literature review and case study of Vacuum Enhanced Recovery (VER) technology, also known as bioslurping, is presented in this project paper. The literature review was conducted to investigate historic and current VER design and pilot study practices. The case study presents the field activities and results of an actual VER pilot study conducted at an operating retail petroleum facility. The results of the pilot study were used to assess the feasibility of VER technology to remediate hydrocarbon impacted soil and groundwater and to design a full scale VER system for the site. The pilot study results indicated a high level of contaminant mass removal from the subsurface and a large zone of groundwater influence. Consequently, VER was deemed an acceptable remediation technology for the case site. The full scale system was designed to draw 8.60 am3/min (300 acfm) of air and 57 Lpm of water from the subsurface at an operating vacuum of 457 mm Hg. (18 in. Hg). The system employed a 30 hp oil-sealed liquid vacuum pump attached to eight individually plumbed extraction wells. An initial mass removal rate of 1,430 kg/day of total petroleum hydrocarbons was estimated. VER is a relatively new subject in the field of remediation engineering. Additional research should focus on methods to limit the uncertainty in design due to site-specific heterogeneities, less onerous methods of applying numerical modeling to simulate multiphase flow in the subsurface, and additional case studies to improve pilot study protocol and VER system design.Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .N37. Source: Masters Abstracts International, Volume: 39-02, page: 0552. Adviser: Stan Reitsma. Thesis (M.A.Sc.)--University of Windsor (Canada), 2000.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".