An Integrated System Planning and Process Control System for Marine Wastewater Management
Bibliographic record
Abstract
An ideal combination of both system planning and process control can greatly reduce system cost and maximize economic and environmental benefits associated with marine oily wastewater treatment.If appropriate process control is not implemented during the system planning procedure, there might be potential benefits lost because traditional planning tends to be more conservative and less risk-taking.However, such integration is oftentimes complicated by many factors such as multi-scale nature of decisions, the lack of knowledge of process dynamics and control, and various types of uncertainties.How to more accurately couple process control with system planning has been a major roadblock in the wide application of the marine oily wastewater treatment systems.To date, there has been no such attempt made to investigate the feasibility and efficacy of coupling system planning and process control on marine oily wastewater treatment.This research therefore aims at demonstrating the possible integration of process control with traditional system planning by using neural networks, genetic algorithm, multistage principle, and Monte Carlo simulation.A case study that is related to offshore water use and wastewater treatment is carried out to demonstrate the efficacy of the proposed approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".