Recommendation Method of Ore Blending Based on Thermodynamic Principle and Adaptive Step Size

  • Huan Wang
  • Bingyang Shen
  • Yuxing Gao
  • Yuning Cao
  • Xiaojuan Ban
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 768)


In the modern metallurgical enterprises, it is necessary to design ore concentrates and production parameters according to the production, which is called ore blending. This paper presents a method of ore blending recommendation using thermodynamic principle and adaptive step size. The whole ore blending process is divided into three parts: blanking recommendation, parameter calculation and result correction. In the part of blanking recommendation, a case-based recommendation system is used to recommend the concentrate list. In the parameter calculation section, the SOMA is used to calculate the concentrate phase. And combined with the method of concentrate phase adjustment, an intelligent algorithm for rapid and accurate calculation of concentrate phase is proposed. Then the smelting parameters are calculated based on thermodynamic principle. In the result correction part, the high accuracy ore blending recommendation is achieved by combining adaptive step size with thermodynamic calculation software. Through the combination of three parts, a fast recommendation method for intelligent ore blending is proposed. The rationality and effectiveness of the method are verified by experiments.


Ore blending recommendation Concentrate phase calculation Swarm intelligence algorithm SOMA Adaptive step size 



This work was supported in part by The National Key Research and Development Program of China (Grant No. 2016YFB0700502, 2016YF- B1001404) and National Natural Science Foundation of China (No. 61572075).


  1. 1.
    Li, Z., Cui, Z.: Multi-objective optimization of ore blending based on genetic algorithm. J. Guangxi Univ. (National Science Edition) 05, 1230–1238 (2013)Google Scholar
  2. 2.
    Kai-Lin, H.U., Ping, L.I.: The optimized scheduling for iron-making bulk ore blending process based on improved ant colony optimization. J. Shanghai Jiaotong Univ. 45(8), 1105–1112 (2011)Google Scholar
  3. 3.
    Zhang, R., Lu, J., Zhang, G.: A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces. Eur. J. Oper. Res. 215(1), 194–203 (2011)CrossRefGoogle Scholar
  4. 4.
    Ke, L., He, X., Ye, Y., He, H.: Current research situation and development trend of ore blending optimization technology. China Min. 26(01), 77–82 (2017)Google Scholar
  5. 5.
    Huang, R., Lv, X.W., Bai, C.G., et al.: Solid state and smelting reduction of Panzhihua ilmenite concentrate with coke. Can. Metall. Q. 51(4), 434–439 (2012)CrossRefGoogle Scholar
  6. 6.
    Yu, W., Tang, Q., Chen, J., Sun, T.: Thermodynamic analysis of carbothermic reduction of a high-phosphorus oolitic iron ore by factsage. Int. J. Miner. Metall. Mater. 23(10), 1126–1132 (2016)CrossRefGoogle Scholar
  7. 7.
    Wu, S.L., Oliveira, D., Dai, Y.M., et al.: Ore-blending optimization model for sintering process based on characteristics of iron ores. J. Miner. Metall. Mater. 19(3), 217–224 (2012)CrossRefGoogle Scholar
  8. 8.
    Song, C.Y., Kai-Lin, H.U., Ping, L.I.: Modeling and scheduling optimization for bulk ore blending process. J. Iron Steel Res. (English Edition) 19(9), 20–28 (2012)CrossRefGoogle Scholar
  9. 9.
    Dokmanic, I., Parhizkar, R., Ranieri, J., et al.: Euclidean distance matrices: essential theory, algorithms, and applications. IEEE Signal Process. Mag. 32(6), 12–30 (2015)CrossRefGoogle Scholar
  10. 10.
    Ramon, L.D.M., Mcsherry, D., Bridge, D., Leake, D., Smyth, B.: Retrieval, reuse, revision and retention in case-based reasoning. Knowl. Eng. Rev. 20(3), 215–240 (2005)CrossRefGoogle Scholar
  11. 11.
    Chakraborty, A., Chakraborty, M.: Multi criteria genetic algorithm for optimal blending of coal. OPSEARCH 49(4), 386–399 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Savic, M., Nikolic, D., Mihajlovic, I., et al.: Multi-criteria decision support system for optimal blending process in zinc production. Miner. Process. Extr. Metall. Rev. 36(4), 267–280 (2015)CrossRefGoogle Scholar
  13. 13.
    Deep, K., Dipti: A new hybrid Self Organizing Migrating Genetic Algorithm for function optimization. In: IEEE Congress on Evolutionary Computation, pp. 2796–2803 (2007)Google Scholar
  14. 14.
    Kadlec, P., Raida, Z.: Self-organizing migrating algorithm for optimization with general number of objectives. In: Radioelektronika, pp. 1–5. IEEE (2012)Google Scholar
  15. 15.
    Zanghirati, G., Zanni, L., Frassoldati, G.: New adaptive step size selections in gradient methods. J. Indus. Manag. Optim. 4(2), 299–312 (2017)CrossRefMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Huan Wang
    • 1
  • Bingyang Shen
    • 2
  • Yuxing Gao
    • 2
  • Yuning Cao
    • 2
  • Xiaojuan Ban
    • 2
  1. 1.School of Civil and Resource EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina

Personalised recommendations