Heuristic Adaptive Control of Waste Gas Exhausting in Coke Making

• Autorzy: Kačur Ján , Flegner Patrik , Durdán Milan , Laciak Marek

Identyfikatory

DOI

10.12913/22998624/128395

• Języki publikacji: EN

Abstrakty

EN

Stabilization of the carbon monoxide (CO) in the waste gas is a common technical problem in many industrial plants. Stabilization can be performed continuously by regulating the fuel input or by regulating the exhaust gas draught. This paper proposes an adaptive control system for CO stabilization in waste gases based on a discrete controller. Heuristic adaptation of a discrete controller is based on continuous optimization of controller parameters. The advantage of this solution is that the control system does not need to perform the identification of the controlled system repeatedly. The parameters of the controller are dynamically optimized during the production process. By regulating the under-pressure, we change the amount of air supplied to the combustion chambers, which affects the combustion of gaseous fuel and also the concentration of CO in the waste flue gas. The control algorithm was verified for the combustion process in coke making. The proposed control achieved good stabilization quality when verified in simulation and also in an industry operation. The CO level at which the waste gas temperature was highest was selected as the setpoint. It was found that the stabilization of CO in waste gas to lower values is possible to achieve higher waste gas temperature and by that, higher temperatures in heating chambers.

Słowa kluczowe

EN

heuristic adaptation; adaptive control; discrete controller; dynamic optimization; coking; waste gas control; carbon monoxide

PL

sterowanie adaptacyjne; dyskretny kontroler; adaptacja heurystyczna; optymalizacja dynamiczna; spiekanie; kontrola gazów odlotowych; tlenek węgla

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2020
• Tom: Vol. 14, no 4
• Strony: 195--212
• Opis fizyczny: Bibliogr. 49 poz., fig., tab.

Twórcy

autor

Kačur Ján

• Technical University of Košice, Faculty BERG, Institute of Control and Informatization of Production Processes, Němcovej 3, 042 00 Košice, Slovak Republic

autor

Flegner Patrik

• Technical University of Košice, Faculty BERG, Institute of Control and Informatization of Production Processes, Němcovej 3, 042 00 Košice, Slovak Republic

autor

Durdán Milan

• Technical University of Košice, Faculty BERG, Institute of Control and Informatization of Production Processes, Němcovej 3, 042 00 Košice, Slovak Republic

autor

Laciak Marek

• Technical University of Košice, Faculty BERG, Institute of Control and Informatization of Production Processes, Němcovej 3, 042 00 Košice, Slovak Republic

Bibliografia

• 1. Aiping, L., Xuzhi, L., Min, W., Qi, L. Process optimization based on multi-objective optimization model for coking plant production. 2008 27th Chinese Control Conference, 2008, 511–515. http:// dx.doi.org/10.1109/chicc.2008.4605196
• 2. Åström K. J., Hägglund T., PID Controllers: Theory, Design, and Tuning, 2nd Ed., Instrument Society of America, USA, 343 p., 1995.
• 3. Åström, K. J., Wittenmark, B. Adaptive Control: Second Edition. Dover Publications, 2008.
• 4. Bobál, V., Böhm, J., Fessl, J., Macháček, J. Digital Self-tuning cotrollers, Algorithms, Implementation and Applications, Springer-Verlag, London, 2005.
• 5. Bobál, V., Böhm, J., Prokop, R., Fessl, J., Praktické aspekty samočinně se nastavujících regulátorů: algoritmy a implementace, 1. Vydání, Vsoké učení technické v Brně, 242 p., VUTIUM, 1999.
• 6. Djalal, M. R, Faisal, F. Design of optimal PID controller for three phase induction motor based on ant colony optimization. Sinergi, 24(2), Universitas Mercu Buana, 2020. 125–132. http://dx.doi. org/10.22441/sinergi.2002.2.006
• 7. Gambier, A. Digital PID controller design based on parametric optimization. 2008 IEEE International Conference on Control Applications. IEEE, 2008, 792–797. http://dx.doi.org/10.1109/ cca.2008.4629671
• 8. He, W., Wang, Y., Liu, S., Li, J., Zhang, R. Combustion Furnace Control Based on FeedforwardFuzzy Decoupling. 2019 Chinese Control Conference (CCC). 2019, 2809–2814. http://dx.doi. org/10.23919/ChiCC.2019.8866397
• 9. Hein, M., Kaiser, M. Environmental Control and Emission Reduction for Coking Plants. Air Pollution - A Comprehensive Perspective, Aug. 2012, 235–280. http://dx.doi.org/10.5772/48275
• 10. Horn, B. C. Optimal Self-Tuning of PID Controllers. 1988 American Control Conference, IEEE, 15-17 June 1988, 2362–2367. http://dx.doi. org/10.23919/acc.1988.4790122
• 11. Hrubina, K. Optimálne riadenie I. a II. Edičné stredisko VŠT v Košiciach, 1985.
• 12. Hu, W. Studies on PID controller tuning and selfoptimizing control. Ph.D. Thesis, School of Electrical & Electronic Engineering, Nanyang Technological University, 2012, 216 p. http://dx.doi. org/10.32657/10356/48149
• 13. Kemal, A., Bowman, C. T. Active adaptive control of combustion. Proceedings of International Conference on Control Applications, 1995, 667–672. http://dx.doi.org/10.1109/cca.1995.555817
• 14. Kostúr, K. Optimalizácia procesov, ES TU Košice, 1991.
• 15. Kozina, A., Píša, M., Šplíchal, B., Koksárenství, SNTL Praha, 474 p., 1973.
• 16. Li, Y., Wang, X., & Tan, J. Introduction of Advanced Control Strategy for Coking Flue Gas Processing. 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), 2016, 1000– 1005. http://dx.doi.org/10.1109/icisce.2016.216
• 17. Li, Z., Qingyang, X., Shibo, J., Jiangning, L. Coking flue temperature RBF, neural network model. The 27th Chinese Control and Decision Conference (2015 CCDC), 2015, 5885–5887. http://dx.doi. org/10.1109/ccdc.2015.7161862
• 18. Maršík, J., Strejc, V. Application of identificationfree algorithms for adaptive control. Automatica, 25(2), 273–277, Elsevier, 1989. http://dx.doi. org/10.1016/0005-1098(89)90081-2
• 19. Maršik, J., Strejc, V. Heuristic Adaptive Process Computer Control. Digital Computer Applications to Process Control, Proceedings of the 7th IFAC/IFIP/IMACS Conference, Vienna, Austria, 17–20 September 1985, IFAC Symposia Series, 1986, 347–352. http://dx.doi. org/10.1016/b978-0-08-032554-5.50050-1
• 20. Nenchev, V., Hans, C. A. Optimal adaptive predictive control of a combustion engine. 2015 European Control Conference (ECC), 2015, 1409–1413. http://dx.doi.org/10.1109/ecc.2015.7330736
• 21. Neto, C. A., Embiruçu, M. Tuning of PID Controllers: An Optimization-Based Method. IFAC Proceedings Volumes, 33(4), 2000, 367–372. http:// dx.doi.org/10.1016/s1474-6670(17)38271-x
• 22. Nomura, S., Arima, T., Dobashi, A., Doi, K. Coking Pressure Control by Selective Crushing of High Coking Pressure Coal. ISIJ International, 51(9), 2011, 1425–1431. http://dx.doi.org/10.2355/isijinternational.51.1425
• 23. Özden, Ü. The investigation of the effect of coking time and temperature on metallurgical coke production by using a mixture of coking and noncoking coal. Mineral Processing on the Verge of the 21st Century, 2017, 403–407. http://dx.doi. org/10.1201/9780203747117-69
• 24. Padmanabhan, K. T., Bowman, C. T., Powell, J. D. An adaptive optimal combustion control strategy. Combustion and Flame, 1995, 100(1-2), 101–110. http://dx.doi.org/10.1016/0010-2180(94)00081-3
• 25. Pfannstiel, D., Isermann, R. Selftuning Combustion Control for a Furnace with Low Power. Advanced Control of Chemical Processes, Selected Papers from the IFAC Symposium, Toulouse, France, 14-16 October 1991, IFAC Symposia Series, 1992, 11–16. http:// dx.doi.org/10.1016/b978-0-08-041267-2.50007-3
• 26. Píša, M. Výroba koksu, STNL Praha, 1978.
• 27. Pragash, S. M., Natarajan, R. A. Particle Swarm Optimization Based PID Controller For Hemispherical Tank Liquid Level Process. Journal of Xidian University, 14(6), 2020. 117–128. http:// dx.doi.org/10.37896/jxu14.6/014
• 28. Qiao, D., Mu, N., Liao, X., Le, J., Yang, F. Improved evolutionary algorithm and its application in PID controller optimization. Science China, Information Sciences, Letter, Vol. 63, 199205:1–199205:3, Science China Press and Springer-Verlag GmbH Germany, Part of Springer Nature, 2020. 1–3 http:// dx.doi.org/10.1007/s11432-019-9924-7
• 29. Sadaki, J., Tanaka, K., Naganuma, Y. Automatic coking control system. Proceedings of IEEE International Conference on Control and Applications, IEEE, September 13-16. 1993 Vancouver, B.C., 1993, 1–7. http://dx.doi.org/10.1109/ cca.1993.348348
• 30. Sahraian, M., Kodiyalam, S. Tuning PID controllers using error-integral criteria and numerical optimization. 6th Symposium on Multidisciplinary Analysis and Optimization., American Institute of Aeronautics and Astronautics, Inc., AIAA Meeting papers on a disc, 1996. 237–246. http://dx.doi. org/10.2514/6.1996-4009
• 31. Skurikhin, V. I., Zhiteckij, L.S., Procenko, N. M., Jakoveako, L. P. Adaptive Digital Control of Furnace Temperature Conditions for Thermal Process. Control Engineering Practice, 2(6), Elsevier, 1994, 1077–1080. http://dx.doi.org/10.1016/0967- 0661(94)91850-3.
• 32. Su, C., Shi, H., Li, P., Cao, J. Advanced Control in a Delayed Coking Furnace. Measurement and Control, 48(2), 2015, 54–59. http://dx.doi. org/10.1177/0020294015569259
• 33. Trimm, D. L. Control of coking. Chemical Engineering and Processing: Process Intensification, 18(3), 1984, 137–148. http://dx.doi.org/10.1016/0255- 2701(84)80003-3
• 34. Tsumura, K., Tsuda, K., Fujisaki Y. Decentralized Adaptive Control of Coke Oven Batteries, IFAC Proceedings Volumes, vol. 45(23), Elsevier, 2012, 266–267. http://dx.doi.org/10.3182/20120910-3- jp-4023.00043
• 35. Virozub, I. V., Lejbovič R. E. Výpočty koksovacích pecí a pochodov koksovania, STNL Praha, 1977.
• 36. Vlad, M. Environmental Pollution Control in Coking Plants Situated in Proximity of Urban Areas. SGEM 2012 12th International Multidisciplinary Scientific GeoConference, 4, 2012, 303–310. http:// dx.doi.org/10.5593/sgem2012/s17.v4009
• 37. Wang, Y., Jiao, Y., Cai, B., Zhao, J. Method on PID controller optimization based on the data-driven technique. 2017 Chinese Automation Congress (CAC). IEEE, 2017, 2567–2571. http://dx.doi. org/10.1109/cac.2017.8243208
• 38. Wei, J. A Novel PID Controller Parameter Optimization Method. Applied Mechanics and Materials, 738- 739, 2015, 1077–1081. http://dx.doi.org/10.4028/ www.scientific.net/amm.738-739.1077
• 39. Wu, M., Cao, W., Chen, X., She, J. Intelligent Optimization and Control of Coking Process. In: Intelligent Optimization and Control of Complex Metallurgical Processes, 3, Springer, Singapore, 2020, 83–133. http://dx.doi.org/10.1007/978-981-15-1145-5_3
• 40. Xu, J., Feng, X. Design of adaptive fuzzy PID tuner using optimization method. Proceedings of the 5th World Congress on Intelligent Control and Automation, June 15-19, 2004. Hangzhou, PR. China (IEEE Cat. No.04EX788). IEEE, 2004, 2454–2458. http://dx.doi.org/10.1109/wcica.2004.1342035
• 41. Yijian, L., Yanjun, F. Optimization design of PID controller parameters based on improved E. Coli foraging optimization algorithm. 2008 IEEE International Conference on Automation and Logistics. IEEE, 2008, 227–231. http://dx.doi.org/10.1109/ ical.2008.46361512362-2367
• 42. Yimchunger, A. T., Acharya, D., Das, D. K. Particle Swarm Optimization based PID-Controller Design for Volume Control of Artificial Ventilation System. Proceedings of 2020 IEEE Calcutta Conference (CALCON), 278–282. Part No.: CFP20O01-ART. http://dx.doi.org/10.1109/calcon49167.2020.9106480
• 43. Zanoli, S. M., Barchiesi, D., Astolfi, G., Barboni, L. Advanced control solutions to increase efficiency of a furnace combustion process. 2013 European Control Conference (ECC). 2013, 4316–4321. http://dx.doi.org/10.23919/ecc.2013.6669817
• 44. Zhang, J. Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace. ISA Transactions, 67, 2017, 208– 214. http://dx.doi.org/10.1016/j.isatra.2016.11.006
• 45. Zhang, R., Li, P., Ren, Z., Wang, S. Combining predictive functional control and PID for liquid level of coking furnace. 2009 IEEE International Conference on Control and Automation, 2009, 314–318. http://dx.doi.org/10.1109/icca.2009.5410157
• 46. Zhang, R., Wang, S. Support vector machine based predictive functional control design for output temperature of coking furnace. Journal of Process Control, 18(5), 2008, 439–448. http://dx.doi. org/10.1016/j.jprocont.2007.10.008
• 47. Zhang, W., Huang, D., Wang, Y., Wang, J. Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace. Chinese Journal of Chemical Engineering, 16(4), 2008, 590–598. http://dx.doi.org/10.1016/s1004-9541(08)60126-3
• 48. Zhao, J., Xi, M. Self-Tuning of PID Parameters Based on Adaptive Genetic Algorithm. EMCEME 2019, IOP Conference Series: Materials Science and Engineering 782, 042028, IOP Publishing Ltd., 2020. 1–8. http://dx.doi.org/10.1088/1757- 899X/782/4/042028
• 49. Ziegler, J. G., Nichols, N. B. Optimum settings for automatic controllers. Transactions of the ASME, 64, 1942, 759–768.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).