American Institute of Mathematical Sciences

Advanced
December  2015, 8(6): 1223-1237. doi: 10.3934/dcdss.2015.8.1223

Intelligent control model and its simulation of flue temperature in coke oven

Gongfa Li 1, , Wei Miao 1, , Guozhang Jiang 1, , Yinfeng Fang 2, , Zhaojie Ju 2, and  Honghai Liu 2,

1. 

College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China, China, China
2. 

Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth PO1 3HE, United Kingdom, United Kingdom, United Kingdom

Received  May 2015 Revised  September 2015 Published  December 2015

In this paper, one-variable linear regression mathematical model of top of regenerator temperature and flue temperature in machine side is built using the linear regress theory. The parameters of ARX model is determined by identification method of least square method and the mathematical model of flue temperature control is established. Applying the basis cascade control theory, system adopts flue temperature and coal flue gas flow as controlled parameters of host circuit and subsidiary circuit respectively. The compound Fuzzy-PID control strategy is presented combined with the characteristics of temperature system after analyzing the conventional PID control algorithm and fuzzy control algorithm. Using step signal and periodic signal to simulate the conventional PID and compound Fuzzy-PID algorithm, the result has indicated: Compound Fuzzy PID control algorithm combines with the advantages of fuzzy control and PID control algorithm, including fast response speed and strong anti-interference ability. When external conditions change, the fuzzy PID compound control can show the strong adaptability and robustness which effectively improve the stability of the control system.
Keywords: simulate., cascade control, compound Fuzzy-PID control, Flue temperature, linear regress theory.
Mathematics Subject Classification: Primary: 93B52, 93C10; Secondary: 93C4.
Citation: Gongfa Li, Wei Miao, Guozhang Jiang, Yinfeng Fang, Zhaojie Ju, Honghai Liu. Intelligent control model and its simulation of flue temperature in coke oven. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1223-1237. doi: 10.3934/dcdss.2015.8.1223
References:
[1]

S. Appari, R. Tanaka, C. Y. Li, S. Kudo, J. Hayashi, M. J. Vinod, H. Watanabe and K. Norinaga, Predicting the temperature and reactant concentration profiles of reacting flow in the partial oxidation of hot coke oven gas using detailed chemistry and a one-dimensional flow model, Chemical Engineering Journal, 266 (2015), 82-90. doi: 10.1016/j.cej.2014.12.041.  Google Scholar

[2]

W. H. Chen, M. R. Lin, T. S. Leu and S. W. Du, An evaluation of hydrogen production from the perspective of using blast furnace gas and coke oven gas as feedstock, International Journal of Hydrogen Energy, 36 (2011), 11727-11737. doi: 10.1016/j.ijhydene.2011.06.049.  Google Scholar

[3]

S. K. Das, K. M. Godiwalla and S. P. Mehrotra, A mathematical model for prediction of physical properties of the coke oven charge during carbonization, High Temperature Materials and Processe, 26 (2007), 43-57. Google Scholar

[4]

R. Fabbri, R. Johnson, S. Novo and C. Núñez, On linear-quadratic dissipative control processes with time-varying coefficients, Discrete and Continuous Dynamical Systems - Series S (DCDS-S), 33 (2013), 193-210. doi: 10.3934/dcds.2013.33.193.  Google Scholar

[5]

X. W. Gao and Y. P. Zhao, The fuzzy adaptive PID in the simulation of coke oven temperature control, Journal of Northeastern University, 27 (2006), 1067-1070. Google Scholar

[6]

Y. N. Guo, D. W. Gong and J. Cheng, Coke oven heating temperature fuzzy control system, in Proceedings of the IEEE International Conference on Control Applications, 2004, 195-198. Google Scholar

[7]

D. R. Jenkins and M. R. Mahoney, Programmed heating of coke ovens for increased coke size, Ironmaking and Steelmaking, 37 (2010), 570-577. doi: 10.1179/030192310X12706364542948.  Google Scholar

[8]

G. Z. Jiang, T. T. He, G. F. Li and J. Y. Kong, Intelligent control of coke oven, in Proceedings of International Conference on Logistics Systems and Intelligent Management, Vol 1, IEEE, 2010, 512-515. doi: 10.1109/ICLSIM.2010.5461371.  Google Scholar

[9]

J. L. Karst, E. Petit and J. P. Gaillet, Optimization of coke oven charging by use of a mathematical model, Revue de Metallurgie. Cahiers D'Informations Techniques, 101 (2004), 447-452. doi: 10.1051/metal:2004186.  Google Scholar

[10]

E. T. Ko, S. K. Hwang and J. S. Lee, A combustion control modeling of coke oven by swarm-based fuzzy system, in Proceedings of SICE-ICASE International Joint Conferenc, 2006, 2503-2507. doi: 10.1109/SICE.2006.314682.  Google Scholar

[11]

Q. Lei, J. Y. Li, M. Wu and Y. He, The application of multi-objective differential evolution algorithm in the combustion process of coke oven, in Proceedings of the 32nd Chinese Control Conference, 2013, 8395-8400. Google Scholar

[12]

Q. Lei and M. Wu, Fuzzy optimization control of the temperature for the heating process in coke oven based on co-evolution, in Proceedings of the 26th Chinese Control Conference, 2007, 420-424. Google Scholar

[13]

Q. Lei, M. Wu, W. H. Cao and S. Y. Hou, An intelligent integrated method for soft-sensing of the flue temperature in coke oven and its application, Journal of East China University of Science and Technology (Natural Science Edition), 32 (2013), 726-766. Google Scholar

[14]

G. F. Li, Y. S. Gu, J. Y. Kong, G. Z. Jiang and L. X. Xie, Intelligent diagnosis of coke oven heating production, Sensors and Transducers, 16 (2012), 226-232. Google Scholar

[15]

G. F. Li, Y. He, G. Z. Jiang, J. Y. Kong and L. X. Xie, Research on the air-fuel ratio intelligent control method for coke oven combustion energy saving, in Proceedings of 2nd International Conference on Frontiers of Manufacturing and Design Science, 2011, 2873-2877. doi: 10.4028/www.scientific.net/AMM.121-126.2873.  Google Scholar

[16]

G. F. Li, P. X. Qu, J. Y. Kong, G. Z. Jiang, L. X. Xie, P. Gao, Z. H. Gao and Y. He, Coke oven intelligent integrated control system, Applied Mathematics and Information Sciences, 7 (2013), 1043-1050. Google Scholar

[17]

G. F. Li, W. T. Xiao, G. Z. Jiang, J. Y. Kong, J. Liu, Y. K. Zhang and F. W. Cheng, Soft-sensing model of coke oven flue temperature, Sensors and Transducers, 161 (2013), 265-270. Google Scholar

[18]

G. F. Li, Y. S. Gu, J. Y. Kong, G. Z. Jiang and L. X. Xie, Intelligent control of coke oven air-fuel ratio, International Review on Computers and Software, 7 (2012), 1262-1267. Google Scholar

[19]

G. F. Li, J. Y. Kong, G. Z. Jiang, L. X. Xie, Z. G. Jiang and G. Zhao, Air-fuel ratio intelligent control in coke oven combustion process, INFORMATION-An International Interdisciplinary Journal, 15 (2012), 4487-4494. Google Scholar

[20]

W. Lin, Y. H. Feng and X. X. Zhang, Numerical study of volatiles production, fluid flow and heat transfer in coke ovens, Applied Thermal Engineerin, 81 (2015), 353-358. doi: 10.1016/j.applthermaleng.2015.02.056.  Google Scholar

[21]

W. S. Lin, L. Zhang and A. Z. Gu, Effects of hydrogen content on nitrogen expansion liquefaction process of coke oven gas, Cryogenics, 61 (2014), 149-153. doi: 10.1016/j.cryogenics.2014.01.006.  Google Scholar

[22]

Q. Lü and E. Zuazua, Robust null controllability for heat equations with unknown switching control mode, Discrete and Continuous Dynamical Systems - Series S (DCDS-S), 34 (2014), 4183-4210. doi: 10.3934/dcds.2014.34.4183.  Google Scholar

[23]

G. Nicolas and V. R. Tatiana, Prediction of coke oven wall pressure, Fuel, 139 (2015), 692-703. Google Scholar

[24]

K. P. Prachethan, A. Kinlekar, K. Mallikarjuna and M. Ranjan, Coal pyrolysis and kinetic model for nonrecovery coke ovens, Ironmaking and Steelmaking, 38 (2011), 608-612. Google Scholar

[25]

R. Razzaq, C. S. Li and S. J. Zhang, Coke oven gas: Availability, properties, purification and utilization in china, Fuel, 113 (2013), 287-299. doi: 10.1016/j.fuel.2013.05.070.  Google Scholar

[26]

D. L. Russell, Control via decoupling of a class of second order linear hybrid systems, Discrete and Continuous Dynamical Systems - Series S (DCDS-S), 7 (2014), 1321-1334. doi: 10.3934/dcdss.2014.7.1321.  Google Scholar

[27]

G. Sergei and M. Yuri, A simple non-linear model of immune response, Chaos, 16 (2013), 125-132. doi: 10.1016/S0960-0779(02)00232-1.  Google Scholar

[28]

K. Tsuda, Reduction in coke oven heat consumption through improved fuel valve adjustment, in Proceedings of IFAC Workshop on Automation in the Mining, Mineral and Metal Industries, 2012, 132-133. Google Scholar

[29]

H. T. Wang, W. J. Yang, J. H. Zhou, Z. H. Wang, J. Z. Liu and K. F. Cen, Calculation and analysis on evaporation and mixing characteristics of droplets in high temperature flue, Journal of Zhejiang University (Engineering Science), 45 (2011), 878-884. Google Scholar

[30]

W. Wang, M. Wu, W. H. Cao and Q. Le, Fuzzy-expert control based on combination grey prediction model for flue temperature in coke oven, Control and Decision, 25 (2010), 185-190. Google Scholar

[31]

M. Wu, Q. Lei and W. H. Cao, Flue temperature fuzzy control for coke oven heating process based on multi-operative modes analysis, Journal of Central South University (Science and Technology), 39 (2008), 155-161. Google Scholar

[32]

M. Wu, Q. Lei, W. H. Cao and J. H. She, Integrated soft sensing of coke-oven temperature, Control Engineering Practice, 19 (2011), 1116-1125. doi: 10.1016/j.conengprac.2011.06.001.  Google Scholar

[33]

M. Wu, Y. X. Liu, W. H. Cao and Q. Lei, Research and application of intelligent optimization control system for coke oven heating combustion process, Journal of metallurgical automatio, 30 (2006), 25-28. Google Scholar

[34]

W. T. Xiao, G. F. Li, H. H. Liu, G. Z. Jiang, Z. Liu, D. S. Chen, W. L. Ding, W. Miao and Z. Li, Soft sensor system of coke oven flue temperature based on CBR and PCA-RBFNN, Computer Modelling and New Technologies, 18 (2014), 951-958. Google Scholar

[35]

Z. L. Zhang, B. Q. Lin, G. M. Li and Q. Ye, Coke oven gas explosion suppression, Safety Sciencel, 55 (2013), 81-87. doi: 10.1016/j.ssci.2012.12.006.  Google Scholar

[36]

J. Y. Zhang, X. H. Zhang, Z. Chen and L. Li, Thermodynamic and kinetic model of reforming coke-oven gas with steam, Energy, 35 (2010), 3103-3108. doi: 10.1016/j.energy.2010.03.050.  Google Scholar

[37]

M. D. Zheng and F. Q. Ning, Research on coke oven heating control system, Journal of Dalian University Technology, 41 (2001), 442-445. Google Scholar

show all references

References:
[1]

S. Appari, R. Tanaka, C. Y. Li, S. Kudo, J. Hayashi, M. J. Vinod, H. Watanabe and K. Norinaga, Predicting the temperature and reactant concentration profiles of reacting flow in the partial oxidation of hot coke oven gas using detailed chemistry and a one-dimensional flow model, Chemical Engineering Journal, 266 (2015), 82-90. doi: 10.1016/j.cej.2014.12.041.  Google Scholar

[2]

W. H. Chen, M. R. Lin, T. S. Leu and S. W. Du, An evaluation of hydrogen production from the perspective of using blast furnace gas and coke oven gas as feedstock, International Journal of Hydrogen Energy, 36 (2011), 11727-11737. doi: 10.1016/j.ijhydene.2011.06.049.  Google Scholar

[3]

S. K. Das, K. M. Godiwalla and S. P. Mehrotra, A mathematical model for prediction of physical properties of the coke oven charge during carbonization, High Temperature Materials and Processe, 26 (2007), 43-57. Google Scholar

[4]

R. Fabbri, R. Johnson, S. Novo and C. Núñez, On linear-quadratic dissipative control processes with time-varying coefficients, Discrete and Continuous Dynamical Systems - Series S (DCDS-S), 33 (2013), 193-210. doi: 10.3934/dcds.2013.33.193.  Google Scholar

[5]

X. W. Gao and Y. P. Zhao, The fuzzy adaptive PID in the simulation of coke oven temperature control, Journal of Northeastern University, 27 (2006), 1067-1070. Google Scholar

[6]

Y. N. Guo, D. W. Gong and J. Cheng, Coke oven heating temperature fuzzy control system, in Proceedings of the IEEE International Conference on Control Applications, 2004, 195-198. Google Scholar

[7]

D. R. Jenkins and M. R. Mahoney, Programmed heating of coke ovens for increased coke size, Ironmaking and Steelmaking, 37 (2010), 570-577. doi: 10.1179/030192310X12706364542948.  Google Scholar

[8]

G. Z. Jiang, T. T. He, G. F. Li and J. Y. Kong, Intelligent control of coke oven, in Proceedings of International Conference on Logistics Systems and Intelligent Management, Vol 1, IEEE, 2010, 512-515. doi: 10.1109/ICLSIM.2010.5461371.  Google Scholar

[9]

J. L. Karst, E. Petit and J. P. Gaillet, Optimization of coke oven charging by use of a mathematical model, Revue de Metallurgie. Cahiers D'Informations Techniques, 101 (2004), 447-452. doi: 10.1051/metal:2004186.  Google Scholar

[10]

E. T. Ko, S. K. Hwang and J. S. Lee, A combustion control modeling of coke oven by swarm-based fuzzy system, in Proceedings of SICE-ICASE International Joint Conferenc, 2006, 2503-2507. doi: 10.1109/SICE.2006.314682.  Google Scholar

[11]

Q. Lei, J. Y. Li, M. Wu and Y. He, The application of multi-objective differential evolution algorithm in the combustion process of coke oven, in Proceedings of the 32nd Chinese Control Conference, 2013, 8395-8400. Google Scholar

[12]

Q. Lei and M. Wu, Fuzzy optimization control of the temperature for the heating process in coke oven based on co-evolution, in Proceedings of the 26th Chinese Control Conference, 2007, 420-424. Google Scholar

[13]

Q. Lei, M. Wu, W. H. Cao and S. Y. Hou, An intelligent integrated method for soft-sensing of the flue temperature in coke oven and its application, Journal of East China University of Science and Technology (Natural Science Edition), 32 (2013), 726-766. Google Scholar

[14]

G. F. Li, Y. S. Gu, J. Y. Kong, G. Z. Jiang and L. X. Xie, Intelligent diagnosis of coke oven heating production, Sensors and Transducers, 16 (2012), 226-232. Google Scholar

[15]

G. F. Li, Y. He, G. Z. Jiang, J. Y. Kong and L. X. Xie, Research on the air-fuel ratio intelligent control method for coke oven combustion energy saving, in Proceedings of 2nd International Conference on Frontiers of Manufacturing and Design Science, 2011, 2873-2877. doi: 10.4028/www.scientific.net/AMM.121-126.2873.  Google Scholar

[16]

G. F. Li, P. X. Qu, J. Y. Kong, G. Z. Jiang, L. X. Xie, P. Gao, Z. H. Gao and Y. He, Coke oven intelligent integrated control system, Applied Mathematics and Information Sciences, 7 (2013), 1043-1050. Google Scholar

[17]

G. F. Li, W. T. Xiao, G. Z. Jiang, J. Y. Kong, J. Liu, Y. K. Zhang and F. W. Cheng, Soft-sensing model of coke oven flue temperature, Sensors and Transducers, 161 (2013), 265-270. Google Scholar

[18]

G. F. Li, Y. S. Gu, J. Y. Kong, G. Z. Jiang and L. X. Xie, Intelligent control of coke oven air-fuel ratio, International Review on Computers and Software, 7 (2012), 1262-1267. Google Scholar

[19]

G. F. Li, J. Y. Kong, G. Z. Jiang, L. X. Xie, Z. G. Jiang and G. Zhao, Air-fuel ratio intelligent control in coke oven combustion process, INFORMATION-An International Interdisciplinary Journal, 15 (2012), 4487-4494. Google Scholar

[20]

W. Lin, Y. H. Feng and X. X. Zhang, Numerical study of volatiles production, fluid flow and heat transfer in coke ovens, Applied Thermal Engineerin, 81 (2015), 353-358. doi: 10.1016/j.applthermaleng.2015.02.056.  Google Scholar

[21]

W. S. Lin, L. Zhang and A. Z. Gu, Effects of hydrogen content on nitrogen expansion liquefaction process of coke oven gas, Cryogenics, 61 (2014), 149-153. doi: 10.1016/j.cryogenics.2014.01.006.  Google Scholar

[22]

Q. Lü and E. Zuazua, Robust null controllability for heat equations with unknown switching control mode, Discrete and Continuous Dynamical Systems - Series S (DCDS-S), 34 (2014), 4183-4210. doi: 10.3934/dcds.2014.34.4183.  Google Scholar

[23]

G. Nicolas and V. R. Tatiana, Prediction of coke oven wall pressure, Fuel, 139 (2015), 692-703. Google Scholar

[24]

K. P. Prachethan, A. Kinlekar, K. Mallikarjuna and M. Ranjan, Coal pyrolysis and kinetic model for nonrecovery coke ovens, Ironmaking and Steelmaking, 38 (2011), 608-612. Google Scholar

[25]

R. Razzaq, C. S. Li and S. J. Zhang, Coke oven gas: Availability, properties, purification and utilization in china, Fuel, 113 (2013), 287-299. doi: 10.1016/j.fuel.2013.05.070.  Google Scholar

[26]

D. L. Russell, Control via decoupling of a class of second order linear hybrid systems, Discrete and Continuous Dynamical Systems - Series S (DCDS-S), 7 (2014), 1321-1334. doi: 10.3934/dcdss.2014.7.1321.  Google Scholar

[27]

G. Sergei and M. Yuri, A simple non-linear model of immune response, Chaos, 16 (2013), 125-132. doi: 10.1016/S0960-0779(02)00232-1.  Google Scholar

[28]

K. Tsuda, Reduction in coke oven heat consumption through improved fuel valve adjustment, in Proceedings of IFAC Workshop on Automation in the Mining, Mineral and Metal Industries, 2012, 132-133. Google Scholar

[29]

H. T. Wang, W. J. Yang, J. H. Zhou, Z. H. Wang, J. Z. Liu and K. F. Cen, Calculation and analysis on evaporation and mixing characteristics of droplets in high temperature flue, Journal of Zhejiang University (Engineering Science), 45 (2011), 878-884. Google Scholar

[30]

W. Wang, M. Wu, W. H. Cao and Q. Le, Fuzzy-expert control based on combination grey prediction model for flue temperature in coke oven, Control and Decision, 25 (2010), 185-190. Google Scholar

[31]

M. Wu, Q. Lei and W. H. Cao, Flue temperature fuzzy control for coke oven heating process based on multi-operative modes analysis, Journal of Central South University (Science and Technology), 39 (2008), 155-161. Google Scholar

[32]

M. Wu, Q. Lei, W. H. Cao and J. H. She, Integrated soft sensing of coke-oven temperature, Control Engineering Practice, 19 (2011), 1116-1125. doi: 10.1016/j.conengprac.2011.06.001.  Google Scholar

[33]

M. Wu, Y. X. Liu, W. H. Cao and Q. Lei, Research and application of intelligent optimization control system for coke oven heating combustion process, Journal of metallurgical automatio, 30 (2006), 25-28. Google Scholar

[34]

W. T. Xiao, G. F. Li, H. H. Liu, G. Z. Jiang, Z. Liu, D. S. Chen, W. L. Ding, W. Miao and Z. Li, Soft sensor system of coke oven flue temperature based on CBR and PCA-RBFNN, Computer Modelling and New Technologies, 18 (2014), 951-958. Google Scholar

[35]

Z. L. Zhang, B. Q. Lin, G. M. Li and Q. Ye, Coke oven gas explosion suppression, Safety Sciencel, 55 (2013), 81-87. doi: 10.1016/j.ssci.2012.12.006.  Google Scholar

[36]

J. Y. Zhang, X. H. Zhang, Z. Chen and L. Li, Thermodynamic and kinetic model of reforming coke-oven gas with steam, Energy, 35 (2010), 3103-3108. doi: 10.1016/j.energy.2010.03.050.  Google Scholar

[37]

M. D. Zheng and F. Q. Ning, Research on coke oven heating control system, Journal of Dalian University Technology, 41 (2001), 442-445. Google Scholar

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