Microwave Torrefaction and Densification of Oat Hulls for Heat and Power Production
Bibliographic record
Abstract
Current environmental problems are indicating that we have to opt for renewable and cleaner energy sources, however economic challenges must be addressed. The work described in this thesis investigated the effect of microwave torrefaction of oat hulls, a new technology to upgrade the physiochemical characteristics of agricultural waste for fuel use in heat or power generation. \nMicrowave torrefaction was carried out at temperature levels of 225, 255, and 285ºC, and residence times of 3, 6, and 9 min. It was determined that based on the temperature level or severity of torrefaction treatment, the lignocellulosic structure of the biomass, and physiochemical characteristics were modified. The higher the severity, and residence time, the higher the lignocellulosic degradation; providing an increment in the heating value and ease of grinding, but a decrease in the bulk density and binding characteristics. Moreover, the study of torrefied biomass pellet quality in means of; pellet unit density, tensile strength, heating value, and moisture adsorption determined that mild torrefaction treatments not only enhanced biomass heating values and hydrophobicity, but increased the tensile strength by allowing lignin to act as a natural binder.\nThese results indicate a new advantage of the microwave torrefaction, and allow knowledge contribution. Furthermore, a technoeconomic analysis of a small scale 36,900 t annum-1 microwave torrefaction pellet plant using the lab data; 255ºC for 3 min and 71% mass yield, determined the project feasibility at industrial scale. It was concluded that microwave torrefaction presents a high capital investment when compared to traditional heating methods, however such investment could be justified by the increase in production, and commercialization of liquid by-products. \nThis thesis shows how agricultural waste or biomass could be upgraded to produce biofuels in the province of Saskatchewan. The thesis also presented for the first time data on technoeconomic analysis of microwave equipment cost, and electricity use. The results of the investigation hope to help with knowledge contribution towards the implementation and further research of microwave torrefaction systems for biomass fuel upgrading.
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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".