Causal Multivariate Forecasting of Diesel Hydrotreating Variables on a Reproducible Synthetic Benchmark

This study evaluates a causal, six-target, 30 min forecasting component for diesel hydrotreating using campaign-separated synthetic data, not a deployed digital twin. An empirical, engineering-constrained simulator generated 30 original campaigns; the recovered code reproduced their datasets byte-for-byte but the simulator did not implement mechanistic balances. The archived AURA-SSM hybrid combines a frozen Ridge anchor, a released measurement observer, multiscale context, bounded corrections, and conditional empirical risk weights; despite its identifier, it has no learned state-space layer. On two previously inspected evaluation domains, frozen NRMSE was 0.16996 and 0.18085, versus ten-degradation means of 0.19170 and 0.20490. These retrospective improvements over leading frozen controls were below the prespecified 3% practical threshold; adaptive Ridge was more accurate on average. Additional matched controls, CRISP/TimeFormer adaptations, and a prespecified 16-campaign synthetic evaluation clarified limitations. On new common/modified simulator structures, AURA-SSM achieved ten-degradation NRMSE of 0.18804/0.20332, outperforming NLinear but not a fixed convex blend; context LightGBM was more accurate with clean measurements. A separately corrected observer passed causal tests and improved prolonged-outage prediction without replacing archived results. Interval undercoverage, lack of mechanistic validation, and absent independent industrial data constrain applicability.