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Ex-situ catalytic upgrading of polyolefins offers a promising circular route to high-value light olefins, yet catalyst deactivation challenges performance prediction. The progressive evolution of catalyst properties obscures quantitative relationships between physicochemical descriptors and stability, limiting accurate forecasting of catalyst behavior. Herein, a tailored HZSM-5 catalyst library with systematically tuned acidity and porosity, spanning fresh and intentionally deactivated samples, was prepared to capture catalyst evolution. On this basis, we developed a physically interpretable Tri-Surface Physically Constrained Regression (Tri-PCR) model that quantitatively links measurable descriptors to deactivated catalyst performance. Using 1-octene as a representative pyrolysis intermediate of polyethylene cracking, phosphorus-modified, severely-steamed HZSM-5 delivers 89 wt% light olefins, including 45 wt% propylene, and maintains performance for more than 10 h at 650°C. Crucially, we show that reduced acid density, not mesoporosity alone, is more consistently associated with suppressing deactivation. By relating catalyst descriptors to pathway-associated reaction channels, the Tri-PCR model accurately predicts product selectivity across fresh and spent catalyst states. Descriptor analysis further uncovers a synergistic design space: moderate acid concentrations with abundant weak acid sites collectively sustain high light-olefin yields during extended operation. Together, this data-driven approach provides design rules for selective and stable zeolites to valorize plastic wastes.
Wang et al. (Fri,) studied this question.