Optimization of Power Train Involving Gas Turbine Driven Compressors and Aerial Coolers
Bibliographic record
Abstract
Multi-objective optimizations were conducted for a compressor station comprising two dissimilar compressor units driven by two dissimilar gas turbines, two coolers of different size, and two parallel pipeline sections to the next station. Genetic algorithms were used in this optimization along with models describing the performance characteristics of gas turbines, compressors, aerial coolers, and downstream pipeline section. Essential in these models is the heat transfer between the gas and soil as it affects the pressure drop along the pipeline, and hence relates back to the coolers and compressor flow/pressure settings. Further investigative techniques were developed to also minimize NOx and CO2e emissions along with total energy consumption, i.e. fuel (used in the driver gas turbines) and electrical energy (used in the electrical fans of the aerial coolers). Two optimization scenarios were conducted: 1) Two-objective optimization of total energy consumption and NOx emission, and 2) Two-objective optimization of total energy consumption and CO2e emission. The results showed that savings in the energy consumption in the order of 5–6% is achievable with slight adjustment to unit load sharing and coolers by-pass/fan speed selections. It appears that most of the savings (around 70–75%) are derived from optimizing the load sharing between the two parallel compressors, while the balance of the savings is realized from optimizing the aerial coolers settings. In order to optimize operation for minimum NOx emission as well, a shift towards employing more of the aerial coolers is required. Preliminary cost analysis was conducted for valuation of balancing between energy consumption vs. emission loading in terms of both NOx and CO2e.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".