Prediction control of <scp> CO <sub>2</sub> </scp> capture in coal‐fired power plants based on <scp>ERIME</scp> ‐optimized <scp>CNN</scp> ‐ <scp>LSTM</scp> ‐multi‐head‐attention
Bibliographic record
Abstract
Abstract Predicting CO 2 concentration in post‐combustion carbon capture (PCC) systems is challenging due to complex operating conditions and multivariate interactions. This study proposes an enhanced RIME algorithm (ERIME) optimization‐based convolutional neural network (CNN)‐long short‐term memory (LSTM)‐multi‐head‐attention (ECLMA) model to improve prediction accuracy. The local outlier factor (LOF) algorithm was used to remove noise from the data, while mutual information (MI) determined time lags, and the smoothed clipped absolute deviation (SCAD) method optimized feature selection. The CNN‐LSTM‐multi‐head‐attention model extracts meaningful features from time series data, and parameters are optimized using the ERIME algorithm. Using a simulated dataset from a 600 MW supercritical coal‐fired power plant, the results showed that after LOF outlier removal, root mean square error (RMSE) and mean absolute error (MAE) improved by 10%–13%. Post‐MI delay reconstruction reduced RMSE to 0.00999 and MAE to 11.6937, with R 2 rising to 0.9929. After variable selection, RMSE and MAE further reduced to 0.00907 and 9.9697, with R 2 increasing to 0.9983. After ERIME optimization, the ECLMA model outperformed traditional models, reducing RMSE and MAE by up to 91.55% and 84.94%, respectively, compared to CNN, and by 85.91% and 69.47%, respectively, compared to LSTM. These results confirm the model's superior accuracy and stability.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".