Online Parameter Optimization-Based Prediction for Converter Gas System by Parallel Strategies
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
Linz Donawitz converter gas (LDG) is one of the most important sources of fuel energy in steel industry, whose reasonable use plays a crucial role in energy saving and environment protection. In practice, online prediction of variation of gas holder level and gas demand by users is fundamental to gas utilization and scheduling activities. In this study, a least square support vector machine-based prediction model combined with the parallel strategies is proposed, in which parameter optimization is realized online by a parallel particle swarm optimization and a parallelized validation method, both being implemented with the use of a graphic processing unit. The experiments demonstrate that the online parameter optimization based model greatly improves the prediction quality compared to the version with the fixed modeling parameters. Furthermore, the parallelized strategies largely reduce the computational cost thus guaranteeing the real-time effectiveness of the practical application.
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