Economic optimization of naphtha pyrolysis furnaces with an integrated chamber–coil model: Profit–emissions trade-offs under carbon pricing

This study develops an integrated chamber–coil model for naphtha pyrolysis furnaces that captures the bidirectional coupling between firebox heat transfer and coil-side cracking kinetics. The model combines a zone-based radiant enclosure formulation with a detailed free-radical reaction network, and is calibrated to plant operation data to enable steady-state prediction of coil-outlet composition together with flue-gas CO 2 emissions over realistic operating ranges. Based on the validated digital-twin framework, an economic optimization is formulated using the burner firing rate and naphtha feed rate as decision variables, subject to a coil-outlet temperature window and product-quality constraints. Three scenarios are examined to quantify profit–emissions trade-offs: (i) revenue maximization, (ii) profit maximization accounting for feed/steam/energy costs, and (iii) profit maximization with an explicit carbon-cost term. Under the carbon-pricing scenario, a representative case with a carbon price of USD 70/t CO 2 yields a co-benefit solution, achieving an olefin-yield increase (＋3.30 wt%) together with a CO 2 reduction (-48.09 kton/yr) and a net-profit increase (＋USD 0.38 million/yr) relative to the baseline operation. In contrast, carbon-unaware optimization tends to favor more aggressive heat supply and results in a markedly higher CO 2 burden without a consistent economic advantage. Overall, the proposed integrated chamber–coil digital twin provides a quantitative basis for selecting operating policies that reconcile economic performance with CO 2 mitigation under carbon pricing. • Integrated radiant chamber–coil model links heat transfer and cracking kinetics. • Economic optimization internalizes carbon pricing, revenues, and utility/feed costs. • Quantifies profit–emissions trade-offs under policy and market variability. • Carbon-unaware operation favors aggressive firing, increasing CO 2 emissions. • Economic optimization enables cost-effective CO 2 emissions reduction.