Physics-informed dynamic hybrid modeling for real-time renewable CO₂ tracking in refinery co-processing

Climate change mitigation necessitates significant reductions in carbon emissions from traditional refining industries. However, accurately attributing renewable bio-feedstock contributions to CO₂ emissions in refinery co-processing remains challenging due to dynamic feedstock variability and complex nonlinear process interactions. Existing static and linear methodologies inadequately address these real-time adaptive demands and intricate nonlinearities. To overcome these shortcomings, we propose a hybrid adaptive modeling framework integrating constrained Recursive Least Squares (RLS) with sparse Generalized Additive Models (GAM), complemented by conformal prediction for uncertainty quantification. Our method adaptively tracks feedstock contributions, ensures physical interpretability through non-negativity constraints. Validated on an industrial dataset of over 86,000 samples, the proposed framework achieves superior predictive accuracy (RMSE: 345.4, R²: 0.950), outperforming 15 baseline methods. More importantly, it delivers interpretable and uncertainty-aware estimates of renewable CO₂ emissions in real time, establishing a practical pathway for transparent and data-driven refinery decarbonization.