Optimal control of carbon capture system in coal‐fired power plant based on fusion of <scp>CNN</scp> ‐ <scp>LSTM</scp> and <scp>EMPC</scp> for <scp>MOGWO</scp> optimization
Bibliographic record
Abstract
Abstract Coal‐fired power plants remain a major source of global CO 2 emissions, posing urgent challenges for climate mitigation. This study proposes an AI‐enabled optimal control strategy for solvent‐based post‐combustion CO 2 capture (PCC), aiming to reduce energy consumption and enhance environmental performance. A deep learning model combining convolutional neural networks and long short‐term memory (CNN‐LSTM) is developed to predict multi‐step CO 2 capture rates. This model is integrated with a multi‐objective grey wolf optimizer (MOGWO)‐based economic model predictive controller (EMPC), which dynamically adjusts key operating variables. Simulation results demonstrate that the proposed control scheme achieves a 12.4% reduction in capture cost and a 10.6% decrease in energy consumption, while maintaining a high CO 2 capture efficiency of 95.3%. These improvements not only optimize process economics while significantly lowering the environmental impact of carbon capture systems. These improvements contribute directly to carbon mitigation, offering a promising pathway for cleaner and more sustainable operation of coal‐based power generation under dynamic industrial conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".