Simulation‐based optimization of dual‐phase vacuum extraction to remove nonaqueous phase liquids in subsurface
Bibliographic record
Abstract
An integrated simulation‐optimization system was developed for supporting decisions of the dual phase vacuum extraction (DPVE) processes. The system coupled a DPVE process simulator, a multivariate regression tool and a nonlinear optimization model into a general framework. A stepwise‐cluster‐analysis technique was provided for establishing a DPVE process forecasting system for describing the relationships between remediation actions and system responses (i.e., total extracted volume of oil/water, elevation distribution of water table, and specific volume of oil). The forecasting system was then embedded into a multiobjective optimization framework, where the objectives were to minimize the operation cost and maximize the remediation efficiency. The constraints include environmental, hydraulic and technical restrictions to the DPVE processes. A case study was conducted for a petroleum‐contaminated site in western Canada. The results from the stepwise cluster analysis indicated that the generated cluster trees could be used for predicting system responses of the DPVE process, given inputs of the operating conditions. The prediction accuracies of the generated cluster trees were verified using randomly generated data sets. The optimum operating conditions could vary significantly under different cost‐efficiency targets. When a stricter environmental target (i.e., the amount of pollutants in subsurface) was concerned, a higher system cost had to be paid; when the cost became a critical factor, the performance of contaminant removal would have to be compromised. The developed system could be used to analyze tradeoffs between system cost and process efficiency in the DPVE operations; it could also support the formulation of an on‐site process‐control system with vacuum levels and extraction rates being the main control variables.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".