Optimum design of integrated liquid recovery plants by variable population size genetic algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Increase in the price of energy sources as well as economic problems have caused cryogenic natural gas plants to become more complex and efficient. After selecting the process configuration, the flow rate, pressure, and temperature of the process fluid streams are determining factors which should be tuned in order to find the optimum condition. Products specification and operating costs of the plant are two significant parameters which should be considered in an optimal design. Moreover, process design limitations contribute to the problem being more difficult. This paper shows how the optimal operating point in an integrated NGL recovery plant can be found through solving a complex constrained optimization problem. A Variable Population size Genetic Algorithm (VPGA) was used for optimization. As well, the role of VPGA algorithm parameters in solving the process design problems is investigated in this study. The analysis showed that the VPGA method has better performance compared to the general GA methods. The plant‐wide net profit increases 12493360 $/year only by changing the selected operating conditions to its optimal value.
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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 | 0.000 |
| 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 it