Among the most energy intensive industries globally, iron and steel are at the fore front, and likewise is their carbon dioxide emission rate. By-product gases have already been established as vital players in providing alternative energy for production units and utility system in the iron and steel industry. Furthermore, it has already been proven that with optimal distribution of these byproduct gases in the iron and steel plants, the energy cost and carbon dioxide emissions can be considerably reduced. Unfortunately, such optimal distribution is not trivial because it involves modelling of production units, industrial boilers, steam turbines, combined heat and power units, and waste heat and energy recovery units. Additionally, significant 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, maximum allowable burner switches and dynamic demands of by-product gases, steam and electricity and varying consuming rates of by-product gases in production units should be taken into account. All these collectively result in a large-scale complex combinatorial problem.
Researchers at the University of Manchester, Dr. Jie Li and Dr. Li Sun in collaboration with Dr. Yujiao Zeng and Professor Xin Xiao at the Institute of Process Engineering, Chinese Academy of Sciences with Dr. Hechang Li at Shougang Jingtang United Iron & Steel Co. Ltd. and Professor Christodoulos A. Floudas at the Texas A&M Energy Institute developed a novel multi-period mixed-integer linear programming model for optimal distribution of byproduct gases, steam, and power in an iron and steel plant. In their work, they purposed to address all the limitations in the existing literature and incorporated all realistic operational features. Their work is currently published in the research journal, Energy.
The research method employed begun with the development of a multi-period mixed integer linear programming model for optimal distribution of byproduct gases, steam and power in production units, boilers, turbines, combined heat and power units, and waste heat and energy recovery units simultaneously. Next, the researchers assumed the consuming rates of byproduct gases in production units to be variables and the demands of by-product gases, steam and electricity in production units to be piecewise constant in order to capture realistic dynamic features. They then introduced new binary variables so as to determine electricity purchase or sale decision with each having different price. 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 were also incorporated to make their model viable.
The authors observed 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. This significant improvement mainly arises from the reduction of the purchased electricity and coal, reduction of gasholder level deviation from the normal level, and reduction of burner on/off switches. In addition, it was observed that zero byproduct gas emission is achieved, gasholder levels are controlled in their safe operational zone at any time, and natural gas is not required to be purchased from the proposed model, which is consistent with those from actual operation.
Yujiao Zeng, Xin Xiao, Jie Li, Li Sun, Christodoulos A. Floudas, Hechang Li. A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant. Energy, volume 143 (2018) pages 881-899.Go To Energy