In-Situ Concentration Measurement of Blended Hydrogen Gas Using Sensor Fusion Enhanced by Machine Learning Model
Bibliographic record
Abstract
Abstract Blending hydrogen (H2) into natural gas via existing pipelines presents a practical method for H2 transportation. However, accurately measuring the H2 concentration in these blends is crucial for safety, operational efficiency, cost-effectiveness, and energy content tracking. This study proposes an effective sensor fusion system for in-site measurement of blended H2 concentration by combining ultrasonic and thermal conductivity (TC) sensors. To test this system, these sensors were installed in a pipeline along with pressure, temperature, and humidity sensors to compensate for environmental factors, while mass flow controllers regulated gas composition. Calibration across the full range of H2 concentrations confirmed the suitability of ultrasonic and TC sensors for H2 measurement. A dataset comprising 185 sensing data points under various environmental conditions was collected to train a machine-learning model for in-site H2 measurement. The evaluation of the model demonstrated higher accuracy in H2 measurement through sensor fusion compared to individual models, with the optimized model exhibiting excellent performance, achieving a coefficient of determination (r2) of 0.98 between actual and measured values. While this study was conducted under limited environmental conditions, it is anticipated that accurate H2 concentration measurement will be feasible in more diverse environments with additional sensor data collected in the future.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".