Collaborative optimization of dynamic early warning and control in desulphurization process via integration of causal inference and temporal features

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.