Advertisement

Intelligent Decoupling Control of Gas Collection and Mixing-and-Pressurization Processes

  • 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 is an important raw material in the metallurgy industry [1]. In the coking process, large amount of by-product gas will be generated from coke-ovens [2], and the process of recycling by-product gas is called gas collecting process. Gas collection involves using gas collectors to collect the gas produced by coke-ovens and sending it where it will be used. Generally, several coke-ovens are in operation at the same time. After the purification of the coal gas, the gas mixing-and-pressurization process of clean coal gas together with clean coal gas is a very important step in the production of steel and nonferrous metals.

References

  1. 1.
    Sadaki J, Tanaka K, Naganuma Y (1993) Automatic coking control system. In: Proceedings of IEEE Conference on Control Applications, pp 531–538Google Scholar
  2. 2.
    Loison R, Foch P, Boyer A (1989) Coke: Quality and Production. Butterworths, LondonGoogle Scholar
  3. 3.
    Jenkins DR (2001) Plastic layer permeability estimation using a model of gas pressure in a coke oven. Fuel 80(14):2057–2065CrossRefGoogle Scholar
  4. 4.
    Gray RJ (1990) Coke Oven Wall Pressures: Measurement, Cause and Effect. Iron and Steel Society Inc, PittsburghGoogle Scholar
  5. 5.
    Wu M, Yan J, She JH, Cao WH (2009) Intelligent decoupling control of gas collection process of multiple asymmetric coke ovens. IEEE Trans Ind Electron 56(7):2782–2792CrossRefGoogle Scholar
  6. 6.
    Gamrat S, Poraj J, Bodys J, Smolka J, Adamczyk W (2016) Influence of external flue gas recirculation on gas combustion in a coke oven heating system. Fuel Process Technol 152:430–437CrossRefGoogle Scholar
  7. 7.
    Weng Y, Gao X (2017) Data-driven robust output tracking control for gas collector pressure system of coke ovens. IEEE Trans Ind Electron 64(5):4187–4198CrossRefGoogle Scholar
  8. 8.
    Coking Experts Group (1978) Coking technologies. Metallurgical Industry Press, Beijing (In Chinese)Google Scholar
  9. 9.
    Wu M, Cao WH, He CY, She JH (2009) Integrated intelligent control of gas mixing-and-pressurization process. IEEE Trans Control Syst Technol 17(1):68–77CrossRefGoogle Scholar
  10. 10.
    Yuan C, Cao WH, Yuan Y, Wu M, Zhang Y (2014) Integrated modeling for gas mixing process based on mechanism and subspace identification. In: The 26th Chinese control and decision conference, pp 1912–1917Google Scholar
  11. 11.
    Li Z (1998) Discussion on methods of stabilizing caloric value of mixed gas used for a heating furnace. Metall Power 2:14–17 (In Chinese)Google Scholar
  12. 12.
    Niemueller T, Zug S, Schneider S, Karras U (2016) Knowledge-based instrumentation and control for competitive industry-inspired robotic domains. K\(\ddot{\rm u}\)nstliche Intelligenz 30(3–4):1–11CrossRefGoogle Scholar
  13. 13.
    Liu J (2018) Intelligent control design and Matlab simulation. Springer, New YorkzbMATHCrossRefGoogle Scholar
  14. 14.
    Zuhrie MS, Munoto, Hariadi E, Muslim S (2018) The design of artificial intelligence robot based on fuzzy logic controller algorithm. IOP Conf Ser: Mater Sci Eng 336(1):012020-1–012020-9CrossRefGoogle Scholar
  15. 15.
    Xie S, Xie Y, Yang C, Gui W, Wang Y (2018) Distributed parameter modeling and optimal control of the oxidation rate in the iron removal process. J Process Control 61:47–57CrossRefGoogle Scholar
  16. 16.
    Cai ZX (1997) Intelligent control: principles, techniques and applications. World Scientific Publishing Co., Ltd., SingaporeGoogle Scholar
  17. 17.
    Åström KJ, Anton JJ, Årzén KE (1986) Expert control. Automatica 22(3):277–286zbMATHCrossRefGoogle Scholar
  18. 18.
    Wang H, Sheng C, Lu X (2017) Knowledge-based control and optimization of blast furnace gas system in steel industry. IEEE Access 5(99):25034–25045CrossRefGoogle Scholar
  19. 19.
    Albusac J, Vallejo D, Castro-Schez JJ, Gzlez-Morcillo C (2018) An expert fuzzy system for improving safety on pedestrian crossings by means of visual feedback. Control Eng Pract 75:38–54CrossRefGoogle Scholar
  20. 20.
    Hou LL, Yang HP, Zhao YJ (1995) Microcomputer control system design for coke oven gas collecting pressure process. Coal Convers 18(2):91–95 (In Chinese)Google Scholar
  21. 21.
    Yang CH, Wu M, Shen DY, Deconinck G (2001) Hybrid intelligent control of gas collectors of coke ovens. Control Eng Pract 9(7):725–733CrossRefGoogle Scholar
  22. 22.
    Mahmoud MS (2018) Fuzzy control, estimation and diagnosis. Springer, New YorkGoogle Scholar
  23. 23.
    Su X, Xia F, Liu J, Wu L (2018) Event-triggered fuzzy control of nonlinear systems with its application to inverted pendulum systems. Automatica 94:236–248MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Precup RE, David RC, Petriu EM (2016) Grey wolf optimizer algorithm-based tuning of fuzzy control systems with reduced parametric sensitivity. IEEE Trans Ind Electron 64(1):527–534CrossRefGoogle Scholar
  25. 25.
    Ahmed MS, Bhatti UL, Al-Sunni FM, El-Shafei M (2001) Design of a fuzzy servo-controller. Fuzzy Sets Syst 124(2):231–147MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Yuvapriya T, Lakshmi P (2017) Design of fuzzy logic controller for reduction of vibration in full car model using active suspension system. Asian J Res Soc Sci Hum 7(3):302–313Google Scholar
  27. 27.
    Osorio R, Alonso JM, Vazquez N, Pinto SE, Sorcia-Vazquez FDJ, Martnez M, Barrera LM (2018) Fuzzy logic control with an improved algorithm for integrated led drivers. IEEE Trans Ind Electron 65(9):6994–7003CrossRefGoogle Scholar
  28. 28.
    Hu X, Xu B, Hu C (2018) Robust adaptive fuzzy control for HFV with parameter uncertainty and unmodelled dynamics. IEEE Trans Ind Electron 65(11):8851–8860CrossRefGoogle Scholar
  29. 29.
    Feng G, Lai C, Kar N (2016) A closed-loop fuzzy logic based current controller for PMSM torque ripple minimization using the magnitude of speed harmonic as the feedback control signal. IEEE Trans Ind Electron 64(4):2642–2653CrossRefGoogle Scholar
  30. 30.
    Eker İ, Torun Y (2006) Fuzzy logic control to be conventional method. Energy Convers Manag 47(4):377–394CrossRefGoogle Scholar
  31. 31.
    Li CH, Liu CS, Li HJ, Zheng ZM (1997) Development and application of a pressure self-optimizing fuzzy control system for gas collector of coke oven. Metall Ind Autom 5:32–34 (In Chinese)Google Scholar
  32. 32.
    Zhou B, Li WY (2003) Application of fuzzy theory on gas collector control. In: International conference on machine learning and cybernetics, pp 2516–2519Google Scholar
  33. 33.
    Fang KL, Zhou HJ, Huang WH (2003) Fuzzy decoupling adjustment for the pressure of coke oven collecting main. In: International conference on machine learning and cybernetics, pp 2605–2608Google Scholar
  34. 34.
    Li H, Yang S, Ren H (2016) Dynamic decoupling control of DGCMG gimbal system via state feedback linearization. Mechatronics 36:127–135CrossRefGoogle Scholar
  35. 35.
    Fang J, Yin R, Lei X (2015) An adaptive decoupling control for three-axis gyro stabilized platform based on neural networks. Mechatronics 27:38–46CrossRefGoogle Scholar
  36. 36.
    Wang Y, Chai T, Fu J, Sun J, Wang H (2013) Adaptive decoupling switching control of the forced-circulation evaporation system using neural networks. IEEE Trans Control Syst Technol 21(3):964–974CrossRefGoogle Scholar
  37. 37.
    Zhu HL, Meng Z, Lu W, Zhu ZN, Zhang D (2015) Control system design for lattice distortion modification test equipment based on fuzzy compensated decoupling. Appl Mech Mater 709:308–311CrossRefGoogle Scholar
  38. 38.
    Gao SZ, Yang J, Wang JS (2014) D-FNN based modeling and BP neural network decoupling control of PVC stripping process. Math Probl Eng 835–892Google Scholar
  39. 39.
    Wang C, Zhao W, Luan Z, Gao Q, Deng K (2018) Decoupling control of vehicle chassis system based on neural network inverse system. Mech Syst Signal Process 106:176–197CrossRefGoogle Scholar
  40. 40.
    Sun W, Yang DP, Yue D, Cheng YH (2000) Intelligent control for pressure system of gas collecting pipe of coke oven. J China Univ Min Technol 29(5):503–505 (In Chinese)Google Scholar
  41. 41.
    Qin B, Wang X, Wu M (2005) Application of multi-agent system to coke ovens. Iron Steel 40(10):25–28 (In Chinese)Google Scholar
  42. 42.
    Liu XQ, Hao R, Tan DJ, Sun W (2000) Application of compensated decoupling arithmetic in discharge header pressure control system. J China Univ Min Technol 29(2):2115–218 (In Chinese)Google Scholar
  43. 43.
    Cao WH, Wu M, Hou SY (2006) Implementation of intelligent decoupling control method for gas mixing and pressurization. J Central South Univ 37(4):780–785 (In Chinese)Google Scholar
  44. 44.
    Lin B (2003) The computer control on gas calorific value. Control Instrum Chem Ind 30(1):75–77 (In Chinese)Google Scholar
  45. 45.
    Qin L (2002) Control for mixed gas calorific value with decoupling and Smith compensator. Control Eng China 9(4):73–75 (In Chinese)Google Scholar
  46. 46.
    Ren HL, Li SY (2004) Fuzzy decoupling control of pressure and heating power for mixed gas. Process Autom Instrum 25(3):65–67 (In Chinese)Google Scholar
  47. 47.
    Jing YH, Fang CZ (1993) Process control. Tsinghua University Press, Beijing (In Chinese)Google Scholar
  48. 48.
    Bristol EH (1966) On a new measure of interaction for multivariable process control. IEEE Trans Autom Control 11(1):133–134CrossRefGoogle Scholar
  49. 49.
    Mizuochi M, Tsuji T, Ohnishi K (2007) Multirate sampling method for acceleration control system. IEEE Trans Ind Electron 54(3):1462–1471CrossRefGoogle Scholar
  50. 50.
    Li CT (2003) Review for commissioning of gas mixing and pressuring station. Metall Power 18(6):31–32 (In Chinese)Google Scholar
  51. 51.
    Bartos FJ (1996) Fuzzy logic reaches adulthood. Control Eng 43(10):50–56Google Scholar
  52. 52.
    Lee SJ, Kwon YA (2007) Study on fuzzy reasoning application for sensory evaluation of sausages. Food Control 18(7):811–816CrossRefGoogle Scholar
  53. 53.
    Meng JE, Mandal S (2016) A survey of adaptive fuzzy controllers: nonlinearities and classifications. IEEE Trans Fuzzy Syst 24(5):1095–1107CrossRefGoogle Scholar
  54. 54.
    Wiktorowicz K (2017) Design of state feedback adaptive fuzzy controllers for second-order systems using a frequency stability criterion. IEEE Trans Fuzzy Syst 25(3):499–510CrossRefGoogle Scholar
  55. 55.
    Wu HC (2007) Using fuzzy sets theory and Black-Scholes formula to generate pricing boundaries of European options. Appl Math Comput 185(1):136–146MathSciNetzbMATHGoogle Scholar
  56. 56.
    Sha TJ (2003) The capability and application of control valves group. China Instrum 20(1):16–19 (In Chinese)Google Scholar
  57. 57.
    Hu Y, Liu G (2016) Intelligent decoupling of gas collector pressure based on internal model control. In: Chinese control and decision conference, pp 4302–4305Google Scholar
  58. 58.
    Wang JS, Xie C (2006) Redial basis function neural network-based robotic joint intelligent decoupling control. In: Wavelet active media technology and information processing, pp 86–91Google Scholar
  59. 59.
    Fu Y, Chai T (2009) Intelligent decoupling control of nonlinear multivariable systems and its application to a wind tunnel system. IEEE Trans Control Syst Technol 17(6):1376–1384Google Scholar
  60. 60.
    Jantzen J (2007) Foundations of fuzzy control. Wiley, ChichesterGoogle 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

Personalised recommendations