A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 7.847, Top 10% (SCI Q1)
    CiteScore: 13.0, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Feng Jin, Jun Zhao, Chunyang Sheng and Wei Wang, "Causality Diagram-based Scheduling Approach for Blast Furnace Gas System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 587-594, Mar. 2018. doi: 10.1109/JAS.2017.7510715
Citation: Feng Jin, Jun Zhao, Chunyang Sheng and Wei Wang, "Causality Diagram-based Scheduling Approach for Blast Furnace Gas System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 587-594, Mar. 2018. doi: 10.1109/JAS.2017.7510715

Causality Diagram-based Scheduling Approach for Blast Furnace Gas System

doi: 10.1109/JAS.2017.7510715

the National Natural Sciences Foundation of China 61473056

the National Natural Sciences Foundation of China 61533005

the National Natural Sciences Foundation of China 61522304

the National Natural Sciences Foundation of China 61603068

the National Natural Sciences Foundation of China U1560102

More Information
  • Rational use of blast furnace gas (BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means (CFCM) clustering algorithm. In this algorithm, not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered. The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.


  • loading
  • [1]
    J. Zhao, W. Wang, K. Sun, and Y. Liu, "A Bayesian networks structure learning and reasoning-based byproduct gas scheduling in steel industry, " IEEE Trans. Automat. Sci. Eng. vol. 11, no. 4, pp. 1149-1154, Oct. 2014. http://ieeexplore.ieee.org/document/6588622/
    X. P. Zhang, J. Zhao, W. Wang, L. Q. Cong, and W. M. Feng, "An optimal method for prediction and adjustment on byproduct gas holder in steel industry, " Expert Syst. Appl., vol. 38, no. 4, pp. 4588-4599, Apr. 2011. http://www.sciencedirect.com/science/article/pii/s0957417410010894
    J. Zhao, Q. L. Liu, W. Wang, W. Pedrycz, and L. Q. Cong, "Hybrid neural prediction and optimized adjustment for coke oven gas system in steel industry, " IEEE Trans. Neural Netw. Learn. Syst. , vol. 23, no. 3, pp. 439-450, Mar. 2012. http://ieeexplore.ieee.org/document/6126048/
    H. N. Kong, E. S. Qi, S. G. He, and G. Li, "MILP model for plant-wide optimal by-product gas scheduling in iron and steel industry, " J. Iron Steel Res. Int., vol. 17, no. 7, pp. 34-37, Jul. 2010. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gtyjxb-e201007007
    H. Ru, F. Gao, Y. F. Xu, and X. H. Guan, "Online strategy for scheduling by-product gas supply, " in Proc. 2013 IEEE International Conference on Information and Automation (ICIA), Yinchuan, China, pp. 1246-1251. http://ieeexplore.ieee.org/document/6720485/
    J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York, USA:Plenum Press, 1981.
    J. Zhao, Z. Y. Han, W. Pedrycz, and W. Wang, "Granular model of long-term prediction for energy system in steel industry, " IEEE Trans. Cybern. , vol. 46, no. 2, pp. 388-400, Feb. 2016. http://www.ncbi.nlm.nih.gov/pubmed/26168454
    H. B. Cao, H. W. Deng, and Y. P. Wang, "Segmentation of M-FISH images for improved classification of chromosomes with an adaptive fuzzy C-means clustering algorithm, " IEEE Trans. Fuzzy Syst. , vol. 20, no. 1, pp. 1-8, Feb. 2012. http://ieeexplore.ieee.org/document/5872671/
    X. Y. Tang, J. Zhao, C. Y. Sheng, and W. Wang, "Long term prediction for generation amount of Converter gas based on steelmaking production status estimation, " in Proc. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, pp. 1088-1095. http://ieeexplore.ieee.org/document/6891775/
    J. Pearl, Causality:Models, Reasoning, and Inference. Cambridge, UK:MIT Press, 2000.
    M. Erwig, and E. Walkingshaw, "Causal reasoning with neuron diagrams, " in Proc. 2010 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Leganes, Philippines, 2010, pp. 101-108. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5635201&filter%3DAND(p_IS_Number%3A5635184)
    F. Thoemmes and K. Mohan, "Graphical representation of missing data problems, " Struct. Equat. Model., vol. 22, no. 4, pp. 631-642, 2015. doi: 10.1080/10705511.2014.937378
    K. Mohan and J. Pearl, "Graphical models for recovering probabilistic and causal queries from missing data, " in Proc. 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014, pp. 1520-1528. http://www.researchgate.net/publication/288688497_Graphical_models_for_recovering_probabilistic_and_causal_queries_from_missing_data
    H. H. Dai, S. Keble-Johnston, and M. Gan, "Reliable knowledge discovery with a minimal causal model inducer, " in Proc. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), Brussels, Belgium, pp. 629-634. http://dl.acm.org/citation.cfm?id=2472970
    F. M. Chen and D. Zhang, "Combining a causal effect criterion for evaluation of facial attractiveness models, " Neurocomputing, vol. 177, pp. 98-109, Feb. 2016. http://www.sciencedirect.com/science/article/pii/S0925231215016641


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(8)

    Article Metrics

    Article views (678) PDF downloads(57) Cited by()


    DownLoad:  Full-Size Img  PowerPoint