Treatment of Petroleum Contaminated Soil using Supercritical Fluid Extraction (SFE) Technology
Bibliographic record
Abstract
In Canada, about 60% of contaminated sites involve petroleum hydrocarbon (PHC) contamination and most of these sites have been abandoned due to contamination. Among current technologies used for soil remediation, supercritical fluid extraction (SFE) is a relatively recent and potentially viable method. The main aim of this research was to investigate the application of SFE for removal of PHCs from contaminated soils. In the first phase, the effects of SFE operational parameters including fluid pressure, fluid temperature, time duration and mode of extraction on the removal efficiency of PHCs from a spiked sandy soil (with diesel fuel with a ratio of 5 wt%) were investigated. SFE experiments were performed at different pressures (15, 33 and 50 MPa) and temperatures (30, 75 and 120 °C). The combination of 10 min static mode followed by 10 min dynamic mode, repeated for 3 cycles (60 min in total) led to the highest PHC removal percentage. According to response surface methodology (RSM), the optimum pressure and temperature were found to be 50 MPa and 69.3 °C, respectively. According to experimental results, the optimum combination of pressure and temperature determined to be 33 MPa and 75 °C; which resulted in the extraction percentages of 99.2%, 91.7% and 86.1% for PHC F2, F3 and F4 fractions, respectively. In the second phase, the influence of several parameters including soil water content, soil pH and addition of modifier on PHCs removals from a field-contaminated sandy soil using SFE were experimentally investigated. SFE experiments were performed at 33 MPa pressure and temperatures of 45 and 75 °C. Three water content levels of 8%, 14% and 20% at two levels of pH 6.5 and 7.5 were investigated. The extraction of total petroleum hydrocarbon fractions (TPHF), the sum of F2, F3, and F4 fractions, decreased due to the increase in the water content from 8% to 20% at both pH 6.5 and 7.5. The difference of extractions of all PHC fractions at pH values of 6.5 and 7.5 were not statistically significant (at p < 0.05 confidence level) at all three water content levels and pH did not have a significant influence on the PHC removal efficiency. Addition of acetone as a modifier (33.7% TPHF removal) was more effective than hexanes (24.3% TPHF removal) to decrease the concentrations of PHCs for the field contaminated soil. In the third phase, the influence of soil texture and grain size on the extraction of PHC fractions was investigated. SFE experiments were performed at 33 MPa pressure and 75 °C temperature. Three types of soils (soil A, B and C) were spiked with diesel fuel with a ratio of 5 wt%. Soil A, B and C had different particle sizes and were categorized as sand, silt loam and clay, respectively. Soil A (sand) which had the largest particle size resulted in the highest TPHF removal percentage while soil C (clay) with the smallest particle size led to the lowest TPHF removal percentage. A higher clay content in soil C resulted in a lower extraction of PHCs. In the fourth phase, the effects of pressure and temperature on the extraction of PHC fractions from a clay soil spiked with diesel fuel with a ratio of 5 wt% were investigated. SFE experiments were performed at three pressures (15, 33 and 50 MPa) and temperatures (30, 75 and 120 °C). According to the statistical analysis including factorial design and RSM, the optimized combination of pressure and temperature was selected at 42.8 MPa and 120 °C; which resulted in the removal percentages of 74.9% and 65.6% for PHC F2 and F3 fractions, respectively. The optimum combination of pressure and temperature based on the experimental results was selected at 33 MPa and 120 °C that led to 70.3%, 58.4% and 32.6% removal of PHC F2, F3 and F4 fractions, respectively.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".