Integration of solar thermal technologies into superheated steam processing in northern climates: A review
Bibliographic record
Abstract
Abstract. Increasing costs of both energy and the fossil fuels from which it is derived, compounded by the environmental concerns surrounding the combustion of petrochemicals has spurred interest in the development and implementation of renewable energy resources. Solar energy is the most abundant renewable resource, but due to technological constraints, has yet to be exploited to a large degree. While methods of direct conversion to electricity have reached experimental efficiencies of 38.8%, commercially available photovoltaic technology is limited to the range of 15%. Conversely, solar thermal conversion technologies have been shown to operate at efficiencies in excess of 80% at varying temperature differentials. For processes such as superheated steam generation, solar thermal technology can provide a reduction in both energy costs and greenhouse gas emissions. Superheated steam (SS) is a valuable process medium, both for its capacity to carry energy and to remove moisture from biological materials and has theoretical applications in lignocellulosic biomass pretreatment, potentially shifting the energy balance in favor of second-generation biofuels. Of the existing technologies for collection of solar thermal energy, the best-suited for implementation into a SS process stream are evacuated tube collectors and solar trough collectors due to their capacity to operate at high enough temperature differentials to produce SS of suitable quality. This paper provides an evaluation of existing solar thermal technologies for their applications in SS generation based on the solar climate in Winnipeg, Manitoba.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".