Data-Driven Cyclic Scheduling Optimization of Industrial Ethylene Furnace Systems for Lower-Carbon Production Considering Cracking and Decoking CO ₂ Emissions

Ethylene cracking furnace systems play an essential role in the petrochemical industry as the primary unit for ethylene production from diverse hydrocarbon feeds. However, significant CO2 emissions are generated by these systems throughout the cracking cycle, influenced by varying feed properties and thereby posing substantial environmental challenges. Variations in yields, coking rates, fuel gas consumption, and CO2 emissions across different feeds require distinct scheduling strategies for furnace groups. These strategies determine feed allocation, processing duration, and decoking sequences. As a result, scheduling decisions strongly affect both the operational efficiency and environmental performance. In this study, high-precision, data-driven models are developed to predict product yields, coking rates, and fuel gas consumption. These models are embedded into a mixed-integer nonlinear programming framework that explicitly accounts for CO2 emissions from both cracking and decoking stages. The resulting model is solved using the Branch-And-Reduce Optimization Navigator (BARON). It maximizes the daily net profit while incorporating full-cycle carbon emission costs. By integrating feed-specific regression models with emission accounting, the framework enables adaptive scheduling strategies that balance economic and environmental objectives. By prioritizing low-emission feeds and optimizing processing duration, the proposed approach achieves a 3.56% reduction in daily CO2 emissions with only a 0.94% decrease in economic benefits compared to conventional scheduling. This work proposes a scheduling strategy for ethylene cracking furnace systems that balances economic benefits with environmental performance, providing crucial technical support for achieving a lower-carbon operation in chemical production processes.