Soil Remediation, Waste Valorization and Biofuels from Cement Kiln Dust Landfills
Bibliographic record
Abstract
Abstract Bioenergy is viewed as a potential solution to anthropogenic greenhouse gas emissions. A sustainable deployment will depend on targeting marginal lands, particularly brownfield and degraded lands. In contrast, industrial remediation is often uneconomical or even desirable, especially if degraded lands passively return to a nature like state. Cement kiln dust landfills, containing saline solids, are such degraded lands that can be remediated biologically. Phytoremediation uses plants to transport a pollutant from contaminated soil into standing biomass. After harvest, it can be processed to separate salt, biofuel and process water. Work showed that salt recovery was preferential for large liquid to solid ratios with little effect for longer durations and higher temperatures. Elevated temperatures require additional energy while co-leaching more biomass, thus reducing total biomass to the kiln. Repeated soaking of smaller volumes of room temperature water also led to effective recovery. A multi stage design was investigated to reduce water consumption. The three stage counter current soaking method produced similar recovery while consuming 25% less water. The product is biomass with sufficient energy density for use as a solid fuel in the cement kiln at a carbon price below $20/t CO2. Long term remediation of cement kiln dust stockpiles is feasible on the century time scale while producing biofuel for the cement plant. Each square kilometer planted can substitute 5% of a cement kiln’s fuel while returning the waste to the kiln. This represents an early opportunity for combined remediation, waste valorization and bioenergy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".