Enhancing Remediation of LNAPL Recovery through a Response-Surface-Based Optimization Approach
Bibliographic record
Abstract
Cost-efficient design for remediation of light nonaqueous phase liquids (LNAPL) from subsurface has become an essential task for site managers in North America. Although many related studies were conducted, there was still lack of efficient and computationally attractive method in such an area. In this study, a response-surface-based optimization approach was developed for enhancing remediation of LNAPL recovery. A two-dimensional numerical model was provided to simulate LNAPL transport during remediation. A dual response regression model was proposed for establishing a linkage between remediation actions and system responses. A nonlinear VFPR management model was then established for generating desired operating conditions. A petroleum-contaminated site in western Canada was used to demonstrate the applicability of the proposed method. The results demonstrated that the DRS model could be used as an effective proxy for simulation models and the optimal solutions from management model led to better cost-effectiveness compared with those from nonoptimized ones. The proposed optimization method was computationally attractive and required simple mathematical manipulations. It was of great practical values for supporting site remediation actions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".