New learnings and strategies for meeting future recovery boiler particulate emission limits with existing electrostatic precipitators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is foreseeable that recovery boiler particulate emission limits in the United States and Canada will continue to get more stringent with time. Because of this, continued improvement of emission control equipment, as well as a better understanding of how operating parameters affect performance, are necessary. Although electrostatic precipitators (ESPs) are often viewed as a mature technology, many improvements in ESP technology continue to be developed. In recent years, academic efforts have improved the understanding of recovery boiler operating conditions on ESP performance. Additionally, advancements in materials, power supplies, and design continue to improve the efficiency and reliability of ESPs. This paper discusses how recovery boiler and electrostatic precipitator (ESP) operating factors affect ESP performance based on process simulations and practical experience, and how these learnings can be implemented to improve future operation of existing ESPs.
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.
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 | 0.000 | 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 it