Plant-Wide Supply-Demand Forecast and Optimization of Byproduct Gas System in Steel Plant
查看参考文献18篇
文摘
|
Considerable energy is consumed during steel manufacturing process. Byproduct gas emerges as secondary energy in the process; however, it is also an atmospheric pollution source if it is released into the air. Therefore, the optimal utilization of byproduct gas not only saves energy but also protects environment. To solve this issue, a forecast model of gas supply, gas demand and surplus gas in a steel plant was proposed. With the progress of energy conservation, the amount of surplus gas was very large. In a steel plant, the surplus gas was usually sent to boilers to generate steam. However, each boiler had an individual efficiency. So the optimization of the utilization of surplus gas in boilers was a key topic. A dynamic programming method was used to develop an optimal utilization strategy for surplus gas. Finally, a case study providing a sound confirmation was given. |
来源
|
Journal of Iron and Steel Research
, International,2013,20(9):1-7 【核心库】
|
DOI
|
10.1016/s1006-706x(13)60148-x
|
关键词
|
byproduct gas
;
supply-demand forecast
;
surplus gas
;
dynamic programming method
|
地址
|
1.
Institute of Thermal and Environmental Engineering,Northeastern University, State Environmental Protection Key Laboratory of Eco-Industry, Liaoning, Shenyang, 110819
2.
School of Mechanical and Power Engineering, Henan Polytechnic University, Henan, Jiaozuo, 454000
|
语种
|
英文 |
文献类型
|
综述型 |
ISSN
|
1006-706X |
学科
|
冶金工业 |
基金
|
Item Sponsored by Science and Technology Research Funds of Liaoning Provincial Education Department of China
|
文献收藏号
|
CSCD:4941337
|
参考文献 共
18
共1页
|
1.
Kim J H. A Novel MILP Model for Plantwide Multiperiod Optimization of Byproduct Gas Supply System in the Iron- and Steel-Making Process.
Trans IChemE,2003,81(8):1015
|
被引
3
次
|
|
|
|
2.
Zhang X P. An Optimal Method for Prediction and Adjustment on Byproduct Gas Holder in Steel Industry.
Expert Systems With Applications,2011,38(4):4588
|
被引
8
次
|
|
|
|
3.
Kong H N. An MILP for Optimization of Byproduct Gases in the Integrated Iron and Steel Plant.
Applied Energy,2010,87(7):2156
|
被引
13
次
|
|
|
|
4.
Kong H N. MILP Model for Plant-Wide Optimal By-Product Gas Scheduling in Iron and Steel Industry.
Journal of Iron and Steel Research, International,2010,17(7):34
|
被引
8
次
|
|
|
|
5.
Mcculloch G A. Application of Operational Research in Production Problems in the Steel Industry.
International Journal of Production Research,1972,10(1):77
|
被引
1
次
|
|
|
|
6.
The Technical Society and the Iron and Steel Institute of Japan. Production and Technology of Iron and Steel in Japan During 2009.
ISIJ International,2010,50(6):777
|
被引
2
次
|
|
|
|
7.
Chan D Y L. The Case Study of Furnace Use and Energy Conservation in Iron and Steel Industry.
Energy,2010,35(4):1665
|
被引
2
次
|
|
|
|
8.
Matsuda K. Energy Saving Study on a Large Steel Plant by Total Site Based Pinch Technology.
Applied Thermal Engineering,2012,43:14
|
被引
5
次
|
|
|
|
9.
Zhang J L. Energy Saving Technologies and Productive Efficiency in the Chinese Iron and Steel Sector.
Energy,2008,33(4):525
|
被引
1
次
|
|
|
|
10.
Sun Wenqiang. Specific Energy Consumption Analysis Model and Its Application in Typical Steel Manufacturing Process.
Journal of Iron and Steel Research, International,2010,17(10):33
|
被引
5
次
|
|
|
|
11.
Larsson M. Reduction of the Specific Energy Use in an Integrated Steel Plant--The Effect of an Optimization Model.
ISIJ International,2003,43(10):1664
|
被引
12
次
|
|
|
|
12.
Yin R Y.
Metallurgical Process Engineering,2011
|
被引
14
次
|
|
|
|
13.
Diamantidis A C. A Dynamic Programming Algorithm for the Buffer Allocation Problem in Homogeneous Asymptotically Reliable Serial Production Lines.
Mathematical Problems in Engineering,2004(3):209
|
被引
2
次
|
|
|
|
14.
Huang Z Y. An Application of Dynamic Programming Principle in Corporate International Investment and Consumption Choice Problem.
Mathematical Problems in Engineering,2010:Article ID 472867
|
被引
1
次
|
|
|
|
15.
Vincenzo M. Application of Dynamic Programming to the Optimal Management of a Hybrid Power Plant With Wind Turbines, Photovoltaic Panels and Compressed Air Energy Storage.
Applied Energy,2012,97:849
|
被引
4
次
|
|
|
|
16.
Balamurugan R. Hybrid Integer Coded Differential Evolution-Dynamic Programming Approach for Economic Load Dispatch With Multiple Fuel Options.
Energy Conversion and Management,2008,49(4):608
|
被引
1
次
|
|
|
|
17.
Senthil K S. A Dynamic Programming Based Fast Computation Hopfield Neural Network for Unit Commitment and Economic Dispatch.
Electric Power Systems Research,2007,77(8):917
|
被引
3
次
|
|
|
|
18.
Guo Z H. The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area.
Mathematical Problems in Engineering,2010:Article ID 684742
|
被引
1
次
|
|
|
|
|