Life cycle-based multi-objective model for optimal gaseous fuel generation and portfolio allocation in gas grids: A strategic decarbonization
Bibliographic record
Abstract
• Life cycle thinking-based multi-objective optimization was employed. • The model accommodates varying decision environments. • Up to 16,000 $/year of carbon tax savings can be achieved by adding biomethane and hydrogen to the gas grid. • Low-carbon gas integration can reduce up to 250 tonnes/year of emissions from key economic sectors. Biomethane and hydrogen are acknowledged as transformative opportunities for decarbonizing the conventional gas grid. Essential to this transformation is the modeling of the gaseous fuel supply chain, particularly with hydrogen and biomethane, offering crucial insights for decision-makers. This study introduces a life cycle thinking-based multi-objective optimization model for the integrated design of biomethane and hydrogen gaseous fuel supply chain networks. The model determines optimal resource allocation for the production of the two fuels, integrating them into the conventional gas network. Moreover, it allocates conventional natural gas, biomethane, and hydrogen optimally across building, industry, and transport sectors, considering the life cycle environmental and economic performance of fuel integration paths. Objective functions include minimization of life cycle emissions and levelized cost of energy while maximizing revenue from fuel sales. Integrating life cycle assessment and cost analysis tools, the optimization model quantifies emissions and life cycle costs for biomethane and hydrogen paths. Results identify Pareto-optimal fuel production paths and portfolios, revealing that integrating the alternative fuels into the current gas grid can significantly reduce emissions (up to 250 tonCO 2eq /year) and generate substantial carbon tax savings (up to $16,250/year). This model is useful for gaseous fuel industry stakeholders, offering a comprehensive view of supply chain costs and detailed insights into emission benefits when integrating alternative fuels into existing gas networks.
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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".