An Intelligent Tool for Evaluating Bids for Circulating Fluidized Bed Boilers
Bibliographic record
Abstract
Circulating fluidized bed (CFB) boilers have gained wide scale acceptance in both the process and utility industries in sizes up to 300 MWe. Their ability to burn opportunity fuels such as petroleum coke has carved out a special niche for CFB boilers in the energy market. Presently more than 600 CFB boilers are either in operation or under construction worldwide. Boiler purchasers have a much wider choice of available designs and manufacturers to choose from, making bid selection more difficult. Even with performance guarantees in place, it is prudent for buyers to evaluate proposed designs in order to fully appreciate the various options and to identify potential problems. CFBCAD© is an intelligent software developed by extensive research into design methodologies for CFB boilers and critical analysis of the design of many CFB boilers manufactured by different companies around the world. The model used considers user-inputted fuel specifications and steam conditions, and performs sizing calculations for the furnace and heat transfer surfaces. A variety of heat transfer surface configurations are available for analysis. It has been used to evaluate the design of some operating plants and to try and predict deviations from design parameters.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".