Engineering Behavior and Characterization of Biomass Ashes using Geotechnical Measurement Techniques
Bibliographic record
Abstract
Biomass, the organic material derived from plants or animals in a biological process, has rapidly become a topic of worldwide interest. The need to find new types of renewable energy sources has led to the use of natural materials as an economic, sustainable and environmental alternative. Biomass can be used to produce biofuel after a conversion process. The combustion of biomass matter –which is mainly produced in industrial plants- becomes biomass ash, which contains macronutrients and micronutrients. Currently, the recycling potential is wasted and most of the biomass ash is dumped without control or just disposed in landfills. The main objective of this research is to provide an environmental solution recycling the biomass ash and reducing the waste. Thus, biomass ashes from two kinds of materials were selected and tested in the laboratory. Wood ash is used in this study since wood is currently one of the largest biomass energy sources that has been applied in engineering in construction of roads, landfills and concrete mixtures. Also, sugarcane bagasse ash was used in this study because sugarcane is the most abundant crop in Louisiana. Bagasse is the residue matter, mostly consisting of the dry fibrous mass remaining after the juice is extracted. Therefore, wood and sugarcane bagasse ashes’ physical and chemical properties were investigated for their characterization such as particle size distribution –sieve and hydrometer, specific gravity, pH, microscope examination using SEM and elemental analysis using EDS. Also, geotechnical tests were conducted such as hydraulic conductivity, one-dimensional consolidation, shear wave velocity using bender elements and thermal conductivity. Samples containing 100% of Ottawa 20-30 sand, wood ash and sugarcane bagasse ash were tested first. Later, mixtures of sand with 2~10% ash were tested in order to compare and evaluate the behavior of those biomass ashes when mixing them with a non-cohesive soil.
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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 | 0.001 | 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".