Aspen plus modeling and multi-objective optimization of low-carbon blast furnace

The steel industry significantly contributes to global carbon dioxide (CO₂) emissions, primarily due to blast furnace ironmaking. In this study, Aspen Plus modeling of a low-carbon blast furnace (LCBF) is addressed by adding hot briquetted iron (HBI) and/or injecting hydrogen-rich gas such as natural gas (NG) and coke oven gas (COG). Notably, the specific operating parameters of Aspen Plus reactor modules in the prescribed zones of LCBF are evaluated by revisiting Rist diagrams, and the replacement ratios of iron/coke are used to predict the carbon reduction potentials. Due to the inevitable tradeoff between carbon reduction and production cost of LCBF, a multi-objective optimization (MOO) algorithm is employed to find the optimal operating scenarios. Through the real-coded genetic algorithm (RCGA) and the methodology for order preference by similarity to the ideal solution (TOPSIS), the optimal solution from the Pareto optimal front could be found. The main results show that the lowest production cost of 311.29 USD/tHM is achieved by injecting NG and COG along with HBI addition under Ukraine's low carbon tax policy, whereas the lowest CO₂ emissions of 397.37 kgCO₂/tHM are obtained by injecting green H₂ combined with HBI addition under Switzerland's high carbon tax policy.