|
|
Recovery of ResidualHeat Integrated Steelworks |
CAI Jiuju,WANG Jianjun,CHEN Chunxia,LU Zhongwu |
State Key Laboratory of BioIndustry, Northeastern University, Shenyang 110004, Liaoning, China |
|
|
Abstract Downtrend of energy consumption per ton crude steel of Chinese steel industry since 1980 is analyzed and the energy consumption was reduced by 48% totally, in which 56% was obtained by directly energy saving and 44% indirectly. However, Chinese steel industry still consumes 112 kg equivalent coal per ton steel more than the advanced level. The calculated residualheat of chinese steel industry is 8.4 GJ for 1 t steel, in which only 25.8% is recovered, so residualheat recovery will play an important role in energy saving. The limitation of heat balance analysis, exergy analysis, energy level analysis and related efficiency indexes is discussed. The change of energy consumption per ton steel product of the process where the residualheat is recovered is the criterion to evaluate the perfection of thermotechnical equipment and rationality of energy utilization. Based on the first and second law of thermodynamics, the expression of exergy, correlated with heat efficiency and energy level difference, is deduced. Exergy efficiency and change of energy consumption per ton product were used as the evaluating indexes to analyze the current technologies for recovery of residualheat in steel industry, and the recommended residualheat recovery mode and its effect is introduced.
|
Received: 01 January 1900
|
|
|
|
[1] |
SHANGGUAN Fang-qin, LI Xiu-ping, ZHOU Ji-cheng, WANG Fang-jie, BU Qing-cai, ZHANG Chun-xia. Strategic research on development of steel scrap resources in China[J]. Iron and Steel, 2020, 55(6): 8-14. |
[2] |
BI Chuan-guang, HUANG Chun-chao, NING Xiao-jun, ZHANG Jian-liang, WANG Guang-wei, PENG Zheng-fu. Best ratio of semi-coke for blast furnace injection[J]. Iron and Steel, 2020, 55(6): 25-32. |
[3] |
JI Xiu-mei1,2,WANG Long1,GAO Ke-wei2,LIU Jie1. Application of ELM to predict plate rolling force[J]. JOURNAL OF IRON AND STEEL RESEARCH , 2020, 32(5): 393-399. |
[4] |
QIN Da-wei, LIU Hong-min, ZHANG Dong, WANG Jun-sheng. Prediction models of coating mass per unit area for hot-dip galvanized strip based on artificial neural network[J]. Iron and Steel, 2020, 55(5): 68-72. |
[5] |
ZHANG Wei-li, WU Sheng-li, HU Zhong-jie. Analysis of affecting factors of activated coke denitrationefficiency for sintering flue gas[J]. Iron and Steel, 2020, 55(5): 109-115. |
[6] |
WU Hao, ZOU Chong, HE Jiang-yong, WANG Wei-an, LIU Zhan-wei, SHI Shuai. Difference of combustion performance between different pyrolytic char and pulverized coal injection in blast furnace[J]. Iron and Steel, 2020, 55(4): 12-19. |
|
|
|
|