The chaotic characteristic of the carbon-monoxide utilization ratio in the blast furnace.

In this paper, carbon monoxide utilization ratio (CMUR) is served as a real-time index to evaluate the energy consumption of blast furnace (BF), and the chaotic analysis method is also presented to study the characteristic of CMUR. Firstly, the time series data measured from two representative BFs are adopted as the sample to investigate the characteristics of CMUR. Secondly, the phase space model of CMUR is reconstructed, and two key related parameters of the model are derived as well. Finally, the value of the chaotic attractor's saturated correlative dimensions in the reconstructed phase space of CMUR is obtained. The result shows that the sample time series of these two BFs have chaos property. Furthermore, the development process of CMUR is also proved to be the chaotic process. It provides a solid foundation for us to further study the chaotic predication and control of CMUR, which helps us to better master the variational tendency of CMUR and provides the effective operation guidance for the BF on the spot to reduce the energy consumption in BF.

[1]  D. Ruelle,et al.  Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems , 1992 .

[2]  Ali Abdi,et al.  A parametric model for the distribution of the angle of arrival and the associated correlation function and power spectrum at the mobile station , 2002, IEEE Trans. Veh. Technol..

[3]  L. Lentati,et al.  All correlations must die: Assessing the significance of a stochastic gravitational-wave background in pulsar timing arrays , 2016, 1606.09180.

[4]  Xuefeng Yan,et al.  Plant-wide process monitoring based on mutual information-multiblock principal component analysis. , 2014, ISA transactions.

[5]  Veena Sahajwalla,et al.  Influence of alkaline (Na, K) vapors on carbon and mineral behavior in blast furnace cokes , 2015 .

[6]  Bellie Sivakumar,et al.  River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .

[7]  J Zhao,et al.  A Two-Stage Online Prediction Method for a Blast Furnace Gas System and Its Application , 2011, IEEE Transactions on Control Systems Technology.

[8]  Sehraneh Ghaemi,et al.  Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks. , 2015, ISA transactions.

[9]  Alexei Pevtsov,et al.  Application of Mutual Information Methods in Time–Distance Helioseismology , 2015, 1501.05597.

[10]  Min Wu,et al.  Modeling of High Temperature Gas Flow 3D Distribution in BF Throat Based on the Computational Fluid Dynamics , 2015, J. Adv. Comput. Intell. Intell. Informatics.

[11]  Shiro Watakabe,et al.  Development of high ratio coke mixed charging technique to the blast furnace , 2006 .

[12]  D. Kugiumtzis.  State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length , 1996, comp-gas/9602002.

[13]  Mohammad. Rasul,et al.  Modelling and analysis of blast furnace performance for efficient utilization of energy , 2007 .

[14]  Mansheng Chu,et al.  Mathematical Modeling and Exergy Analysis of Blast Furnace Operation With Natural Gas Injection , 2013 .

[15]  J. Salas,et al.  Nonlinear dynamics, delay times, and embedding windows , 1999 .

[16]  Haipeng Peng,et al.  Parameter identification of time-delay chaotic system using chaotic ant swarm , 2009 .

[17]  S. P. Garcia,et al.  Multivariate phase space reconstruction by nearest neighbor embedding with different time delays. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[19]  Gao,et al.  Chaotic Identification and Prediction of Silicon Content in Hot Metal , 2005 .

[20]  Yong He,et al.  A Temperature Field Detection System for Blast Furnace Based on Multi-Source Information Fusion , 2013, Intell. Autom. Soft Comput..

[21]  Luis A. Aguirre,et al.  A nonlinear correlation function for selecting the delay time in dynamical reconstructions , 1995 .

[22]  Mehmet Karakose,et al.  An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space. , 2014, ISA transactions.

[23]  Chuanhou Gao,et al.  Time-dependent Fractal Characteristics on Time Series of Silicon Content in Hot Metal of Blast Furnace , 2005 .

[24]  G. Ambika,et al.  A non subjective approach to the GP algorithm for analysing noisy time series , 2006 .

[25]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  P. A. R. Ade,et al.  HIGH-RESOLUTION CMB POWER SPECTRUM FROM THE COMPLETE ACBAR DATA SET , 2008, 0801.1491.

[27]  Xin Wang,et al.  Adaptive synchronization and parameter identification of chaotic system with unknown parameters and mixed delays based on a special matrix structure. , 2013, ISA transactions.

[28]  Kajiwara Yoshimasa,et al.  Investigation of bell-less charging based on full scale model experiments. , 1984 .

[29]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[30]  Cheng-song Liu,et al.  Coke Collapse Model and Collapse Profile Variation Law for Bell-Less Top BF , 2011 .

[31]  Dong Joon Min,et al.  A mass and energy estimation for the hydrogen utilization in the iron-making process , 2011 .