Carbon adsorption and life cycle analysis of natural gas production and utilization
Bibliographic record
Abstract
The increasing greenhouse gas (GHG) emission shows unprecedented challenge that should warn governments and industries to address this global issue. Carbon dioxide is the main source of GHG. The carbon dioxide emission should be curtailed by implementing carbon capture projects, including testing of solid adsorbents. As natural gas production and utilization grow in many industries, the carbon footprint should be quantified by establishing a life cycle analysis using “cradle to grave” methodology. Adsorption is one of many methodologies that separate CO₂ from flue gas. In this thesis, Brunauer Emmett Teller (BET) theory is utilized to measure the specific surface areas of solid adsorbents, such as, activated carbon, polyurethane samples and shales (Permian, Mancos and Eagle Ford) from nitrogen adsorption. Next, CO₂ adsorption is measured by using Micromeritcs 3Flex instrument at 0°C. BET experiments show that the activated carbon had a high specific area of 1138 m²/g. In contrast, Ottawa sand and polyurethane samples had low specific surface area. The specific surface area of Permian, Mancos and Eagle Ford shales ranged from 2-8 m²/g, which mainly depends on the clay content of the shale. Carbon dioxide adsorption was measured from 0.1 to 1 atm at 0°C. Adsorption capacity of the activated carbon was around 85 cm³/g whereas for shales was less than 1 cm³/g. This specific surface area correlated with the adsorption capacity. Life cycle analysis (LCA) is a key methodology to assess the carbon footprint of any substance including natural gas. Natural gas is widely used in many service sectors in United States. In this thesis, LCA is pursued to calculate the carbon footprint of natural gas using the cradle to grave method by constructing a simple model using Excel. This model inputs carbon footprint of fuels in gCO₂/kWh and the energy footprint of used materials during LCA such as steel, cement and steel pipes in kWh/kg. The carbon footprint is calculated during the gas extraction, processing, flaring, transportation, utility and plug and abandonment stages. The LCA duration is set to be 10 years. The total carbon footprint is calculated by quantifying the required energy of each stage in kWh then dividing by the total energy produced during the lifecycle in kWh. Additionally, the cost is calculated by averaging the Henry Hub price and the utility price in Rhode Island, which operates 94% of its electric grid by natural gas power plants. This study shows the carbon footprint of natural gas to be 185.7 gCO2/kWh with an average cost of $0.10/kWh. The utility stage has the highest carbon footprint in LCA of natural gas. This model would help in assessing the carbon intensity of natural gas for any country in the world.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".