Collaborative optimization of dynamic early warning and control in desulphurization process via integration of causal inference and temporal features
Bibliographic record
Abstract
Abstract The inherent time‐lag effects, nonlinear dependencies, and dynamic coupling mechanisms in desulphurization processes pose significant challenges to precise quality prediction and proactive control. This study presents a novel collaborative optimization framework integrating causal inference with temporal feature engineering to achieve dynamic early warning and intelligent control. Methodologically, we first develop a multi‐modal time‐lag estimation approach combining dynamic time warping, Granger causality tests, and time‐delayed mutual information, resolving temporal asynchrony between process variables through dynamic programming and information‐theoretic analysis. Building upon this, a dynamic autoregressive latent variable model (DALM) with Bayesian estimation is established to capture cross‐variable interaction dynamics, enhanced by a long short‐term memory (LSTM)‐based architecture with attention mechanisms for nonlinear temporal dependency modelling. The proposed early‐warning system synergizes anomaly detection (isolation forest/one‐class SVM/autoencoder triad) with NOTEARS‐optimized causal graphs, achieving 93.1% prediction accuracy (AUC = 0.98) through multi‐feature fusion of temporal patterns, causal drivers, and multivariate anomalies. For control optimization, formulate a hybrid MPC strategy incorporating warning‐adaptive penalty terms, demonstrating 89.2% warning probability alignment with quality deviations while the concentrations of SO 2 /H 2 S are kept within a fixed range through restricted reactor feed flow adjustment. Validations confirm the framework reduces unplanned shutdowns by 37% compared to conventional PID control, with R 2 = 0.9144 for SO 2 and 0.9114 for H 2 S concentration predictions. This work provides a systematic solution addressing temporal‐causal decoupling challenges in complex chemical processes, significantly advancing intelligent optimization in pollution control systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".