Modelling, Simulation, Analysis and Optimisation of Thermal Cracking Furnace in Ethylene Manufacturing

Economic efficiency, energy consumption, and CO₂ emissions of thermal cracking furnaces are critical aspects in ethylene production, with the plug flow reactor (PFR) in the radiation section serving as the core component. This thesis aims to enhance the economic performance, reduce energy consumption, and reduce CO₂ emissions of thermal cracking furnaces through modelling, simulation, analysis and optimisation.
A 1-D pseudo-dynamic model of PFR was developed in gPROMS ModelBuilder® to analyse the impact of using steam and CO₂ as diluents in propane cracking. The model was validated with industrial data. The results indicated that the PFR could reach highest annual production at the steam-to-propane ratio 0.2 and reach highest annual profit at the ratio 0.3 when using steam as diluent. CO₂ as a diluent increases run length and annual profit by 10.10%, saves 17.44% energy, and reduces exergy destruction by 20.53% compared to steam. Pure CO2 was recommended as diluent from comparison of pure/mixed diluents in 4 different scenarios. These key findings provide significant operational guidance for existing thermal cracking furnace using steam as diluent and provide insights for future new generation diluents design to reduce the energy consumption and increase the economic benefits for ethylene manufacturing.
Additionally, a hybrid model based on physically consistent machine learning (PCML) was developed for multi-objective dynamic optimisation, balancing profit and CO₂ emissions. This model reduced computational demand to 77 seconds and demonstrated that dynamic adjustment of operating variables with coke formation can enhance profit and reduce CO₂ emissions. The study found that a 28.97% reduction in annual profit could lead to a 42.89% decrease in annual CO₂ emissions. These key findings highlight the great potential for a green ethylene manufacturing based on AI through modelling and optimisation approaches.