Influence of Biochar Particle Size Fractions on Thermal and Mechanical Properties of Biochar-Amended Soil
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Bibliographic record
Abstract
Thermal backfill is essential for projects such as underground crude oil pipelines and crude oil storage tanks to control the heat migration from the source. The properties of locally available soil may not be adequate as thermal backfill and hence need suitable amendment. Biochar is a low thermal conductive material and may contribute to an increase in soil strength. There are no such studies that deal with the thermal properties of biochar-based backfill. Further, the influence of biochar particle size fractions on soil thermal properties has not been reported until now. Therefore, the possibility of biochar as a soil amendment for modifying thermal backfill characteristics is explored in this study. Two types of soils (highly plastic silt and clayey sand) are amended with three biochar content amounts (5%, 10%, and 15%), and three different particle size fractions [coarse (4.7–2 mm), medium (2–0.425 mm), and fine (0.425–0.075 mm)], and their compaction characteristics as well as thermal and mechanical properties are investigated. It was observed that the amendment of biochar in soil reduced the thermal and mechanical properties of the soil. Further, the reduction in soil thermal conductivity and volumetric heat capacity with biochar amendment was more in coarser biochar fraction than finer and medium fractions. In comparison, reduction in unconfined compressive strength (UCS) was more in finer biochar fraction. Additionally, the thermal properties reduction was higher in clayey sand than highly plastic silt. An inverse linear correlation of thermal conductivity with pH and electrical conductivity were observed for both soil biochar mixes. The relationship between the thermal properties and UCS indicates that the medium fraction biochar provides the optimized value for reducing thermal properties and UCS of biochar amended soil (BAS). This proves the efficacy of BAS as thermal backfill.
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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 | 0.001 |
| 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 it