A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant

Yujiao Zeng, Xin Xiao, Jie Li, Li Sun, Christodoulos A. Floudas, Hechangi Li

Research output: Contribution to journalArticle

6 Citations (Scopus)


Byproduct gases, steam and electricity play an important role in providing energy for production units in the iron and steel industry. Optimal distribution of byproduct gases, steam and electricity in an iron and steel plant can significantly decrease energy cost and reduce CO2 emissions. However, such optimal distribution is not trivial because it involves many production units, steam turbines, combined heat and power units, and waste heat and energy recovery units, and several realistic operational features such as byproduct gas mixing, byproduct gas level control in dedicated gasholders, different levels of steam requirement, minimum heating and energy requirement, and maximum allowable burner switches, resulting in a large complex combinatorial problem. In this paper, we develop a novel multi-period mixed-integer linear programming model for optimal distribution of byproduct gases, steam, and power in an iron and steel plant. The consuming rates of byproduct gases are variable. Different byproduct gases are allowed to be mixed to satisfy minimum heating and energy requirement of production units. The steam is specifically classified as high, medium and low pressure. New binary variables are introduced to determine electricity purchase or sale decision with each having different price. The burner switching operation is correctly modeled with fewer binary variables allowing turning on and off at any time. Several important practical features such as fuel selection, gasholder level control, ramp rate variation, piecewise constant generation rates of byproduct gases, and piecewise constant demand profiles of byproduct gases, steam and electricity are also incorporated. The computational results demonstrate that the optimal operating cost is obtained within 2 CPU seconds for an industrial example using the proposed model, which is reduced by 6% compared to that from actual operation.



  • Multi-period
  • Mixed integer linear programming (MILP)
  • Optimization
  • Energy efficiency
  • Iron and steel

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