A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant
Zeng, Yujiao1; Xiao, Xin1; Li, Jie2; Sun, Li2; Floudas, Christodoulos A.3,4; Li, Hechang5
2018-01-15
发表期刊ENERGY
ISSN0360-5442
卷号143页码:881-899
摘要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. (C) 2017 Elsevier Ltd. All rights reserved.
关键词Multi-period Mixed Integer Linear Programming (Milp) Optimization Energy Efficiency Iron And Steel
文章类型Article
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1016/j.energy.2017.10.122
收录类别SCI
语种英语
关键词[WOS]ENERGY EFFICIENCY IMPROVEMENT ; GASOLINE BLENDING OPERATIONS ; MAKING PROCESS ; CHINESE IRON ; MILP MODEL ; RECIPE DETERMINATION ; SUPPLY SYSTEM ; COMBINED HEAT ; INDUSTRY ; DESIGN
WOS研究方向Thermodynamics ; Energy & Fuels
WOS类目Thermodynamics ; Energy & Fuels
项目资助者National Natural Science Foundation of China(21561122001 ; Major Science and Technology Program for Water Pollution Control and Treatment(2015ZX07202013-003) ; 21206174)
WOS记录号WOS:000425565700073
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ipe.ac.cn/handle/122111/23999
专题湿法冶金清洁生产技术国家工程实验室
作者单位1.Chinese Acad Sci, Inst Proc Engn, Div Environm Technol & Engn, Beijing 100190, Peoples R China
2.Univ Manchester, Sch Chem Engn & Analyt Sci, Manchester M13 9PL, Lancs, England
3.Texas A&M Univ, Dept Chem Engn, College Stn, TX 77843 USA
4.Texas A&M Univ, Texas Energy Inst, College Stn, TX 77843 USA
5.Shougang Jingtang United Iron & Steel Co Ltd, Dept Energy & Environm, Tangshan 063200, Peoples R China
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Zeng, Yujiao,Xiao, Xin,Li, Jie,et al. A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant[J]. ENERGY,2018,143:881-899.
APA Zeng, Yujiao,Xiao, Xin,Li, Jie,Sun, Li,Floudas, Christodoulos A.,&Li, Hechang.(2018).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,143,881-899.
MLA Zeng, Yujiao,et al."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 143(2018):881-899.
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