Uncertainty Analysis Applied to Thermodynamic Models and Fuel Properties – Natural Gas Dew Points and Gasoline Reid Vapor Pressures
Bibliographic record
Abstract
A simple, consistent, and self-contained error propagation algorithm was developed using the uncertainty information related to pure component physical properties, binary interaction parameters, and thermodynamic model parameters combined with Monte Carlo simulation along with the Latin Hypercube Sampling (LHS) method. This algorithm is generally applicable to simulate the error propagation in process flow sheets of arbitrary complexity as long as the thermodynamic model parameters encode uncertainty information. In this work, two significant problems related to hydrocarbon processing are studied under the light of uncertainty analysis. First, the injection of a valuable liquid hydrocarbon into an existing natural gas pipeline for transportation was studied in order to find the optimum injection rate of liquid n -butane that can be safely added to the flowing gas without undesired condensation. The main factors considered in this calculation are the hydrocarbon dew point, the natural gas physical properties, and conformity to pipeline specifications. Second, uncertainties on Reid vapor pressure (RVP) calculations were taken into account for the calculation of optimal rate of liquid n -butane blending into gasoline. Gasoline blending is an important operation in refineries where gasoline must be produced with enough volatility for the proper operation of engines in cold climates.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".