Multi-timescale modeling framework for natural gas desulfurization process: Integrating time-lagged causal relationships with machine learning

• A novel temporal causality-driven modeling framework was developed for the natural gas desulfurization process. • Integrated time constant-based temporal hierarchy with time-lagged causality to capture lag effects. • TE-based analysis outperformed other methods in revealing time-lagged causal structures and reducing redundancy. • Causality-informed Random Forest models achieved R ² > 0.8, RMSE 0.80, RMSE < 0.5 mg/m 3 for a 100-min horizon), and improve medium- to long-term accuracy by over 15% compared to other machine learning models. Model interpretation reveals time-dependent influence patterns of key parameters, offering physically consistent insights. The proposed framework provides a practical causality-driven approach for modeling complex industrial processes by bridging physical insights with data-driven predictive power.