Exergy loss minimization for a blast furnace with comparative analyses for energy flows and exergy flows

An optimization model based on material balance and energy balance for a blast furnace iron-making process is established, in which exergy loss minimization is taken as optimization objective. Optimization results are obtained by using sequential quadratic programming method. Effects of coal ratio, top gas temperature, slag basicity and blast parameters on the optimization results are analyzed. The optimization results of the exergy loss minimization objective and the coke ratio minimization objective are analyzed comparatively. The energy flows and exergy flows before and after the optimizations for different objectives are analyzed comparatively. The results show that the total energy flow input, the total exergy flow input and the exergy loss decrease after optimizations. The exergy loss obtained from the optimizations for the exergy loss minimization objective and coke ratio minimization objective decreases by 5.77% and 5.14%, respectively. Above 80% of the total energy input is the energy of fuel and above 80% of the total exergy input is the exergy of fuel. The exergy loss decreases with the increases in coal ratio and blast temperature, and increases with the increases in blast humidity, oxygen enrichment, top gas temperature and slag basicity.

[1]  Hannu Helle,et al.  Optimization of Top Gas Recycling Conditions under High Oxygen Enrichment in the Blast Furnace , 2010 .

[2]  Hannu Helle,et al.  Nonlinear optimization of steel production using traditional and novel blast furnace operation strategies , 2011 .

[3]  Xiang Yin,et al.  A bottom-up analysis of China’s iron and steel industrial energy consumption and CO2 emissions , 2014 .

[4]  M. Emre Ertem,et al.  Energy balance analysis for erdemir blast furnace number one , 2006 .

[5]  Qin Xiao-yon,et al.  Utilization Coefficient Optimization Model for Blast Furnace Iron-Making Process , 2014 .

[6]  Hiroshi Nogami,et al.  Exergy Analysis of Charcoal Charging Operation of Blast Furnace , 2004 .

[7]  P. Venkataraman,et al.  Applied Optimization with MATLAB Programming , 2001 .

[8]  Semida Silveira,et al.  Capturing energy efficiency in European iron and steel production—comparing specific energy consumption and Malmquist productivity index , 2014 .

[9]  N. Pardo,et al.  Prospective scenarios on energy efficiency and CO2 emissions in the European Iron & Steel industry , 2013 .

[10]  Kecun Zhang,et al.  A new SQP method of feasible directions for nonlinear programming , 2004, Appl. Math. Comput..

[11]  Adrian Bejan,et al.  Entropy Generation Minimization, Exergy Analysis, and the Constructal Law , 2013 .

[12]  Michael J. Moran,et al.  Availability analysis: A guide to efficient energy use , 1982 .

[13]  Wang Hongwei,et al.  Increasing proportion of natural lump ores in blast furnace , 2009 .

[14]  Gao Bin.  Optimization Aimed at the Lowest Coke Consumption by Model and Analysis of the Application , 2001 .

[15]  Ibrahim Dincer,et al.  Energy, Entropy and Exergy Concepts and Their Roles in Thermal Engineering , 2001, Entropy.

[16]  Marc A. Rosen,et al.  Second‐law analysis: approaches and implications , 1999 .

[17]  Andreas Johnsson,et al.  A model on CO2 emission reduction in integrated steelmaking by optimization methods , 2008 .

[18]  Wojciech Stanek,et al.  Influence of blast‐furnace process thermal parameters on energy and exergy characteristics and exergy losses , 2006 .

[19]  T. J. Kotas,et al.  The Exergy Method of Thermal Plant Analysis , 2012 .

[20]  Tengfang Xu,et al.  Undertaking high impact strategies: The role of national efficiency measures in long-term energy and emission reduction in steel making , 2014 .

[21]  Mikko Helle,et al.  Multiobjective Optimization of Top Gas Recycling Conditions in the Blast Furnace by Genetic Algorithms , 2011 .

[22]  Hannu Helle,et al.  Multi-objective Optimization of Ironmaking in the Blast Furnace with Top Gas Recycling , 2010 .

[23]  Lynn Price,et al.  A Comparison of Iron and Steel Production Energy Use and Energy Intensity in China and the U.S. , 2014 .

[24]  José Antonio Moya,et al.  The potential for improvements in energy efficiency and CO2 emissions in the EU27 iron and steel industry under different payback periods , 2013 .

[25]  Wojciech Stanek,et al.  Energy and exergy system analysis of thermal improvements of blast‐furnace plants , 2006 .

[26]  Jun-ichiro Yagi,et al.  高炉製銑法,直接製鉄-電気炉法,溶融還元法のエクセルギー解析 , 1988 .

[27]  A. Bejan.  Fundamentals of exergy analysis, entropy generation minimization, and the generation of flow architecture , 2002 .

[28]  Stefan Pauliuk,et al.  The roles of energy and material efficiency in meeting steel industry CO2 targets. , 2013, Environmental science & technology.

[29]  Zhang Wei-bin.  Analysis on Material Flow and Energy Flow of Ironmaking System , 2012 .

[30]  Jan Dahl,et al.  Potential CO2 emission reduction for BF–BOF steelmaking based on optimised use of ferrous burden materials , 2009 .

[31]  WU Sheng-li.  Basic study of modern blast furnace using natural lump ores rationally , 2007 .