An Integrated Simulation-based Process Control and Operation Planning (IS-PCOP) System for Marine Oily Wastewater Management
Bibliographic record
Abstract
An ideal combination of process control and operation planning can reduce system cost and maximize economic and environmental benefits. This research proposed an integrated simulation-based process control and operation planning (IS-PCOP) system by using neural networks, genetic algorithm, multistage principle, and Monte Carlo simulation. It could well link process control and operation planning by simultaneously adopting different time-scales in computation. The hourly process control strategy forwarded the results to the operation planning module where long-term arrangements could be further evaluated. The use of ANN modeling also played a key role in predicting the nonlinear behavior of complex processes. In addition, Monte Carlo simulation yielded a better insight on uncertainties, which may arise from a number of different sources. A case study on marine wastewater management was carried out to demonstrate the efficacy of the proposed approach. Six different treatment standards (i.e., 5, 10, 15, 20, 25, and 30 μg L?1) were examined over a 20-day period and the 20 μg L?1 standard appeared to be the most economic option with a mean net cost of $18 per day. As compared to the traditional operation planning without process control, the integrated approach achieved more economically competitive results. By addressing the uncertainties and expressing the results in probability distributions, the decision makers would have more confidence in making decisions on both short- and long-term operations. It was concluded that the combination of process control and operation planning could help meet the economic objectives and ensure timely completion of the tasks.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".