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Intelligent Optimization and Control of Coking Process

  • Min WuEmail author
  • Weihua Cao
  • Xin Chen
  • Jinhua She
Chapter
Part of the Engineering Applications of Computational Methods book series (EACM, volume 3)

Abstract

Coke, the product of a coking process,  is an important material in the metallurgical industry. In a blast furnace that produces iron, it functions as the main supplier of heat, a reducing reagent, and a support framework for other materials. Its quality directly influences the metallurgical process that occurs in a blast furnace. Coke-oven temperature (COT)  is a key parameter that reflects the thermal state of the whole oven. It directly influences both the quality of coke and the lifetime of an oven [1].

References

  1. 1.
    Smolka J, Slupik L, Fic A, Nowak AJ, Kosyrczyk L (2016) 3-D coupled CFD model of a periodic operation of a heating flue and coke ovens in a coke oven battery. Fuel 165:94–104CrossRefGoogle Scholar
  2. 2.
    Wu M, Lei Q, Cao WH, She JH (2011) Integrated soft sensing of coke-oven temperature. Control Eng Pract 19:1116–1125CrossRefGoogle Scholar
  3. 3.
    Brosilow CB (1978) Inferential control of process control. AIChE J 24:475–484CrossRefGoogle Scholar
  4. 4.
    Ruusunen M, Leivisk K (2004) Fuzzy modeling of carbon dioxide in a burning process. Control Eng Pract 12:607–614CrossRefGoogle Scholar
  5. 5.
    Sbarbaro D, Ascencio P, Espinoza P, Felipe M, Guillermo C (2008) Adaptive soft-sensors for on-line particle size estimation in wet grinding circuits. Control Eng Pract 16:171–178CrossRefGoogle Scholar
  6. 6.
    Abeykoon C (2014) A novel soft sensor for real-time monitoring of the die melt temperature profile in polymer extrusion. IEEE Trans Ind Electron 61(12):7113–7123CrossRefGoogle Scholar
  7. 7.
    Ding Y, Yu JJ, Zhou CH (1994) Quality estimation and supervision for a crude distillation. Pet Process Petrochem 5:23–28 (In Chinese)Google Scholar
  8. 8.
    Bidar B, Sadeghi J, Shahraki F, Khalilipour MM (2017) Data-driven soft sensor approach for online quality prediction using state dependent parameter models. Chemom Intell Lab Syst 162:130–141CrossRefGoogle Scholar
  9. 9.
    Wang D, Liu J, Srinvasan R (2009) Data-driven soft sensor approach for quality prediction in a refining process. IEEE Trans Ind Inpormatics 12:113–121Google Scholar
  10. 10.
    James S, Legge R, Budman H (2002) Comparative study of black-box and hybrid estimation methods in fed-batch fermentation. J Process Control 12:113–121CrossRefGoogle Scholar
  11. 11.
    Zhu B, Chen ZS, He YL, Yu LA (2017) A novel nonlinear functional expansion based PLS (FEPLS) and its soft sensor application. Chemom Intell Lab Syst 161:108–117CrossRefGoogle Scholar
  12. 12.
    Yin S, Zhu XP, Kaynak O (2015) Improved PLS focused on key-performance-indicator-related fault diagnosis. IEEE Trans Ind Electron 62(3):1651–1658CrossRefGoogle Scholar
  13. 13.
    Zhao H, Gao S, He Z, Zeng X, Jin W, Li T (2014) Identification of nonlinear dynamic system using a novel recurrent wavelet neural network based on the pipelined architecture. IEEE Trans Ind Electron 61(8):4171–4182CrossRefGoogle Scholar
  14. 14.
    Mazinan AH (2013) A new algorithm to AI-based predictive control scheme for a distillation column system. Int J Adv Manuf Technol 66(12):1379–1388CrossRefGoogle Scholar
  15. 15.
    Navvab KM, Shahrokh S (2010) A methodology for modeling batch reactors using generalized dynamic neural networks. Chem Eng J 159:195–202CrossRefGoogle Scholar
  16. 16.
    Fortuna L, Giannone P, Graziani S, Xibilia MG (2007) Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery. IEEE Trans Instrum Meas 56:95–101CrossRefGoogle Scholar
  17. 17.
    Dufour P, Bhartiya S, Dhurjati PS, Doyle FJ (2005) Neural network-based software sensor: Training set design and application to a continuous pulp digester. Control Eng Pract 13:135–143CrossRefGoogle Scholar
  18. 18.
    Jemei S, Hissel D, Péra MC, Kauffmann JM (2008) A new modeling approach of embedded fuel-cell power generators based on artificial neural network. IEEE Trans Ind Electron 55:437–447CrossRefGoogle Scholar
  19. 19.
    Khoshgoftaar TM, Hulse JV, Napolitano A (2010) Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors. IEEE Trans Neural Netw 21:813–830CrossRefGoogle Scholar
  20. 20.
    Luo GQ, Wen Z, Chen FH (1998) A flue-oven mathematical model for coke oven. Fuel Chem Process 29:78–82 (In Chinese)Google Scholar
  21. 21.
    Ning FQ, Zhang SF, Yan WF (2004) Generalized predicative control in flue temperature. J UESR China 33:53–55 (In Chinese)Google Scholar
  22. 22.
    Devornique G, Fontchastagner J, Netter D, Takorabet N (2017) Hybrid model: Permeance network and 3-D finite element for modeling claw-pole synchronous machines. IEEE Trans Mag 53(6):7206704-1–7206704-4CrossRefGoogle Scholar
  23. 23.
    Yebi A, Ayalew B (2017) Hybrid modeling and robust control for layer-by-layer manufacturing processes. IEEE Trans Control Syst Technol 25(2):550–562CrossRefGoogle Scholar
  24. 24.
    Egrioglu E, Aladag CH, Yolcu U, Uslu VR, Basaran MA (2009) A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Syst Appl 36:7424–7434CrossRefGoogle Scholar
  25. 25.
    Wang X, Chen J, Liu C, Pan F (2010) Hybrid modeling of penicillin fermentation process based on least square support vector machine. Chem Eng Res Des 4:415–420CrossRefGoogle Scholar
  26. 26.
    Berkutov NK, Stepanov YV, Popova NK (2007) The relation between coke quality and blast-furnace performance. Steel Transl 37(5):438–441CrossRefGoogle Scholar
  27. 27.
    Yurin NI, Morozov OS, Likhacheva OL (2011) Influence of coke quality on blast-furnace performance. Steel Transl 41(11):924–927CrossRefGoogle Scholar
  28. 28.
    Uddin MN, Rebeiro RS (2011) Online efficiency optimization of a fuzzy-logic-controller-based IPMSM drive. IEEE Trans Ind Appl 47(2):1043–1050CrossRefGoogle Scholar
  29. 29.
    Lei Q, Wu M, She JH (2015) Online optimization of fuzzy controller for coke-oven combustion process based on dynamic just-in-time learning. IEEE Trans Autom Sci Eng 12(4):1535–1540CrossRefGoogle Scholar
  30. 30.
    Wang SH, Xu BG, Wang QY, Liu YH (2006) Modified smith predictor and controller for time-delay process with uncertainty. In: Proceedings of the 6th world congress on intelligent control and automation, vol 1, pp 623–627Google Scholar
  31. 31.
    Stratos I, Nikos T, Kimon V (2006) Fuzzy supervisory control of manufacturing systems. IEEE Trans Robot Autom 20(3):379–389Google Scholar
  32. 32.
    Gao XW, Cai XY, Yu XF (2006) Simulation research of genetic neural network base PID control for coke oven heating. In: The sixth world congress on intelligent control and automation, vol 2, pp 21–23Google Scholar
  33. 33.
    Gao XW, Liu H, Zhao YP (2005) Application and research of the fuzzy compound control method in coke oven control system. Control Decis 20(4):434–438 (In Chinese)Google Scholar
  34. 34.
    Romeo LM, Gareta R (2006) Hybrid system for fluling control in biomass boilers. Eng Appl Artif Intell 19(8):919–925CrossRefGoogle Scholar
  35. 35.
    Moslehi Z, Taheri M, Mirzaei A, Safayani M (2018) Discriminative fuzzy C-means as a large margin unsupervised metric learning. IEEE Trans Fuzzy Syst 26(6):3534–3544CrossRefGoogle Scholar
  36. 36.
    Jiang W, Yang T, Shou Y, Tang Y, Hu W (2018) Improved evidential fuzzy C-means method. J Syst Eng Electron 29(1):187–195CrossRefGoogle Scholar
  37. 37.
    Corsini P, Lazzerini B, Marcelloni F (2004) A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data. IEEE Trans Syst, Man, Cybern Part B: Cybern 34:775–783CrossRefGoogle Scholar
  38. 38.
    Huang ZX (1999) Fuzzy k-modes algorithm for clustering categorical data. IEEE Trans Fuzzy Syst 7:446–452CrossRefGoogle Scholar
  39. 39.
    Pedrycz W (1998) Conditional fuzzy clustering in the design of radial basis function neural networks. IEEE Trans Neural Netw 9:601–612CrossRefGoogle Scholar
  40. 40.
    Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278Google Scholar
  41. 41.
    Fujino A, Tobita T, Segawa K, Yoneda K, Togawa A (1997) Elevator group control system with floor-attribute control method and system optimization using genetic algorithms. IEEE Trans Ind Electron 44(4):546–552CrossRefGoogle Scholar
  42. 42.
    Mahapatra NK, Bhunia AK, Maiti M (2005) A multiobjective model of wholesaler-retailers’ problem VIA genetic algorithm. J Appl Math Comput 19(1–2):397–414MathSciNetzbMATHCrossRefGoogle Scholar
  43. 43.
    Srinival M, Patnaik LM (1994) Adapbive probabilities of crossover and mutation in genetic algorithm. IEEE Trans Syst, Man Cybern 24(4):656–667CrossRefGoogle Scholar

Copyright information

© Science Press 2020

Authors and Affiliations

  1. 1.China University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhanChina
  3. 3.School of EngineeringTokyo University of TechnologyTokyoJapan

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