Economic and environmental impact of using hydrogen enriched natural gas and renewable natural gas for residential heating
Bibliographic record
Abstract
Natural gas, a fuel source that provides power generation and heating application, offers significant emissions and efficiency improvements over coal. The majority of natural gas is obtained through non-renewable deposits; however, it is possible to generate methane through the creation of Renewable Natural Gas (RNG). RNG is generated when biogas composed of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> is methanated through the addition of hydrogen. An alternative sustainable pathway, however, is the creation of Hydrogen Enriched Natural Gas (HENG). HENG is created from the addition of hydrogen, in low volume percentages, to create a blend that emits less greenhouse gasses per unit of energy. The hydrogen used to create RNG and HENG can be generated from electrolysis using surplus electricity. Using surplus electricity, during off-peak hours, helps a jurisdiction effectively manage the power grid. As demonstrated through this case study the use of RNG and HENG to be utilized within the natural gas network can create an overall positive impact in any jurisdiction.
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 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".