Natural Gas Compressor Operation Optimization to Minimize Gas Turbine Outboard Bleed Air
Bibliographic record
Abstract
Gas turbine (GT) engines employed in natural gas compressor stations operate in different modes depending on the power, turbine inlet temperature and shaft speeds. These modes apply different sequencing of bleed valve opening on the air compressor side of the engine. Improper selection of the GT and the driven booster compressor operating conditions can lead to larger bleed losses due to wider bleed valve openings. The bleed loss inevitably manifests itself in the form of higher overall heat rate of the GT and greater engine emission. It is therefore imperative to determine and understand the engine and process conditions that drive the GT to operate in these different modes. The ultimate objective is to operate the engine away from the inefficient modes by adjusting the driven booster compressor parameter as well as the overall station operating conditions (i.e. load sharing, control set points, etc.). This paper describes a methodology to couple the operating conditions of the booster compressor to the modes of GT bleed valve opening (and the subsequent air bleed rates) leading to identification of the operating parameters for optimal performance (i.e., best overall efficiency and minimum CO2e emission). A predictive tool is developed to quantify the overall efficiency loss as a result of the different bleed opening modes, and map out the condition on the gas compressor characteristics. One year’s worth of operating data taken from an existing compressor station on TransCanada Pipelines’ Alberta system was used to demonstrate the methodology. This station employs GE-LM1600 gas turbine driving a Cooper Rolls-RFBB-30 centrifugal compressor. The results from the analysis conclusively indicate that there are operating regions on the gas compressor map where losses due to bleed valves are reduced and hence lower CO2 emissions, which presents an opportunity for operation optimization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".