Optimization of Surfactant-Enhanced Aquifer Remediation for a Laboratory BTEX System under Parameter Uncertainty
Bibliographic record
Abstract
This study develops a nonlinear chance-constrained programming (NCCP) model for optimizing surfactant-enhanced aquifer remediation (SEAR) processes. The model can not only address the parameter uncertainty, but provide a reliability level for the identified optimal remediation strategy. To solve the NCCP model, stepwise cluster analysis (SCA) is used to create a set of proxy simulators for quantifying the relationships between operating conditions (i.e., pumping rate) and probabilities of benzene levels in violation of standard. Compared to conventional parametric inference techniques, SCA is independent of prior assumptions for model forms (e.g., linear or exponential ones) and capable of reflecting complex nonlinear relationships between operating conditions and probabilities. To alleviate the computational efforts in the optimization process, the generated proxy simulators are repeatedly called by simulated annealing (SA) to test the feasibility of each potential solution. The implicit of the optimal NCCP solutions is discussed through a laboratory-scale SEAR system where porosity and intrinsic permeability are treated as stochastic parameters. It is observed that well locations, environmental standards, reliability levels and remediation durations would have significant effects on optimal SEAR strategies. By comparing the predicted benzene concentration without and with remediation actions, it is indicated that the optimal SEAR process can guarantee the benzene concentration to meet the environmental standard with a high reliability level.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".