Optimizing Two-Stage Modular Wastewater Plant Expansions Using Numerical Methods and Simulation in A Real Options Context
Why this work is in the frame
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Bibliographic record
Abstract
The optimization of a modular expansion strategy, while extremely relevant in the industrial setting, requires sophisticated numerical modeling for the valuation of even simple scenarios. In this work, we develop both a numerical model and a model based on Monte-Carlo simulation utilizing real options, to provide a methodology for optimizing a plant expansion strategy. Our case study is associated with a wastewater treatment plant expansion; however, the methodologies developed here can be extended to many industrial settings, including mining, oil and gas, and manufacturing. The value of the Monte-Carlo simulation is that it is much more easily understood by practitioners and more versatile in that it can be used to model non-standard processes. The results of both of our models match consistently, essentially validating the Monte-Carlo technique.
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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 | 0.001 |
| 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 it