Decarbonization of gas transmission pipelines via hydrogen blending: A Techno-Environmental case study approach
Bibliographic record
Abstract
This study presents a novel and validated optimization framework to evaluate the performance and techno-environmental and economic impacts of hydrogen blending in steady-state natural gas transmission pipelines. The model investigates hydrogen injection (0 %, 10 %, 20 % and 100 %) and its influence on key parameters, including flow behavior, compressor fuel consumption, pressure limits, emissions, and energy return on investment (EROI). Using a genetic algorithm (GA) implemented in MATLAB, the framework is applied to both generalized cases and a real commercial system, the Coastal GasLink pipeline in Canada. Hydrogen addition significantly alters system behavior, increasing maximum operating pressure from 6.9 to 8.2 MPa and raising compressor fuel use from 2.98 % (100 % NG) to 21.4 % (100 % H 2 ) over an 800 km pipeline. Validation against multiple benchmark studies shows < 1.5 % deviation, confirming model reliability. The study introduces a cost and emission trade-off analysis using a marginal abatement cost curve to assess compressor station electrification strategies. Full electrification reduces emissions by 1.51 MtCO 2 /year but increases operating costs. However, under Canadian incentive structures, the cost of abatement decreases substantially, making large-scale emission reduction economically viable. The analysis also highlights a sharp decline in EROI from 33.56 (100 % NG) to 4.67 (100 % H 2 ), underscoring the need for efficiency-focused infrastructure design. A forward-looking hybrid energy system is proposed, integrating renewables, battery storage, and electrolyzers to enable on-site green hydrogen production and electrified compression. This framework supports infrastructure planning aligned with national decarbonization goals for 2030 and beyond.
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".