Hybrid multiscale forecasting of SRU sulfur gas concentrations using VMD CEEMDAN and optimized PatchTST

The sulfur recovery process in SRUs is highly nonlinear and non-stationary, making accurate forecasting of H₂S and SO₂ concentrations challenging yet crucial for efficient, low-carbon operation. Many existing models fail to handle multi-scale fluctuations, high-frequency noise, and complex variable couplings, limiting their accuracy. This study presents a multi-scale framework combining variational mode decomposition (VMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an enhanced patch time-series transformer with ProbSparse attention (PatchTST-PSA), and projection iterative modeling optimization (PIMO). VMD decomposes the concentration series into intrinsic mode functions, and CEEMDAN suppresses noise while preserving dynamics. PatchTST-PSA captures nonlinear variable interactions, while PIMO optimizes hyperparameters. Experiments on SRU data from an Italian refinery demonstrate that the framework provides improved results in RMSE, MAE, MAPE, and R² compared to six baseline models, highlighting its robustness and industrial relevance.