Impacts of alternative fuel combustion in cement manufacturing: Life cycle greenhouse gas, biogenic carbon, and criteria air contaminant emissions
Bibliographic record
Abstract
Waste products destined for landfills with high calorific value are increasingly being explored for potential use in a variety of industries. One example is the potential use of these wastes in cement kilns to reduce greenhouse gas (GHG) emissions from cement production. This study develops a cradle-to-gate life cycle assessment (LCA) model to estimate the GHG emissions, criteria air contaminants (CAC), and the impact of biogenic carbon accounting methods in biomass-containing alternative fuels (AFs) on life cycle GHG emissions when replacing natural gas with AFs at a cement facility. The proposed AF mixture includes landfill wastes like construction and demolition waste, asphalt shingles, tire fluff, carpet, textiles, and plastics. While many LCAs assume the biogenic fraction's climate impact is carbon-neutral, its actual effects depend on a range of new methods being proposed to account for the climate impacts associated with biogenic carbon. The findings of this study demonstrate a reduction of approximately 7–13% in life cycle GHG emissions. The preliminary estimates suggest that the change to CACs will likely not be materially different from the current use of natural gas. It also emphasizes the importance of accounting for the biogenic fraction in biomass-based AFs, indicating a potential overall reduction of up to 7.2% in life cycle GHG emissions when the biogenic fraction is treated as carbon-neutral. While factoring in the benefits of shorter rotation and longer storage periods results in a 12.7% reduction in life cycle GHG emissions. The LCA model developed in this study holds the potential for broad application among cement facilities that are considering fossil to alternative fuels as part of their GHG emission reduction strategies. • Cement manufacturing contributes ∼8% of global greenhouse gas emissions (GHGs). • Waste materials offer great potential as alternatives to fossil fuels in cement kilns. • A 7–13% decrease in GHGs comes from replacing 50% natural gas with alternative fuels. • GHGs from alternative fuels (AFs) are sensitive to biogenic carbon accounting. • Crediting rotation and storage in biomass-based alternative fuels reduces GHGs by 13%.
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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 | 0.001 | 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".