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Record W2970981794 · doi:10.3968/11133

Water Content of Sweet Natural Gas: A Simplified Formula-Based Approach

2019· article· en· W2970981794 on OpenAlexvenueno aff
Nnaemeka Uwaezuoke, Nkemakolam Chinedu Izuwa, S. I. Onwukwe

Bibliographic record

VenueAdvances in petroleum exploration and development · 2019
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCondensationNatural gasContent (measure theory)ChemistryFlow (mathematics)CO2 contentHydrogenPetroleum engineeringMathematicsThermodynamicsApplied mathematicsEngineeringMathematical analysisPhysicsGeometryOrganic chemistryCarbon dioxide

Abstract

fetched live from OpenAlex

Gas with negligible or zero composition of hydrogen sulphide is sweet natural gas. During production, transportation and processing, the presence of traces of water can cause problems such as ice formation and/0r the problem of gas hydrates, increase in corrosion potential of the gas and two-phase flow problems if condensation occurs. Calculation of the water content at specified temperature and pressure conditions is the first step. A formula based approach of the calculation is proposed, and comparison with existing formulas presented. Application of the proposed formula in the specified ranges of temperature and pressure conditions of 15oC (59oF) to 48.8oC (119.84oF) and 1.07MPa (155.19 psia) to 10MPa (1450.38 psia) is recommended for sweet natural gases. A calculated Mean Absolute Percentage Deviation (MAPD) of 16.4077% from the experimental data is the statistical indicator used for validity check. It predicted better than some existing models which are adaptable under certain conditions. Only a hand-held device is required as the proposed model is highly simplified.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.214
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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