Energy ans exergy analysis of biomass co-firing in pulverized coal power generation
Bibliographic record
Abstract
Biomass co-firing with coal exhibits great potential for large scale utilization of biomass\nenergy in the near future. In the present work, energy and exergy analyses are carried out\nfor a co-firing based power generation system to investigate the impacts of biomass cofiring\non system performance and gaseous emissions of CO2, NOx, and SOx. The power\ngeneration system considered is a typical pulverized coal-fired steam cycle system, while\nfour biomass fuels (rice husk, pine sawdust, chicken litter, and refuse derived fuel) and\ntwo coals (bituminous coal and lignite) are chosen for the analysis. System performance\nis evaluated in terms of important performance parameters for different combinations of\nfuel at different co-firing conditions and for the two cases considered. The results indicate\nthat plant energy and exergy efficiencies decrease with increase of biomass proportion in\nthe fuel mixture. The extent of decrease in energy and exergy efficiencies depends on\nspecific properties of the chosen biomass types. The results also show that the increased\nfraction of biomass significantly reduces the net CO2 emissions for all types of selected\nbiomass. However, gross CO2 emissions increase for all blends except bituminous\ncoal/refuse derived fuel blend, lignite/chicken litter blend and lignite/refuse derived fuel\nblend. The reduction in NOx emissions depends on the nitrogen content of the biomass\nfuel. Likewise, the decrease in SOx emissions depends on the sulphur content of the\nbiomass fuel. The most appropriate biomass in terms of NOx and SOx reduction is\nsawdust because of its negligible nitrogen and sulphur contents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".