Steel industries employ run-out tables [ROTs] in order to cool hot steel billets at desired rates, to achieve different steel grades. They usually consist of air, water or mixed cooling jets coming out of nozzle banks on both sides of the billet. The cooling behaviour of the billet is dependent upon multiple factors, including but not limited to the initial temperature, the nozzle bank distance, the cooling jet flow rate and the velocity of the billet. When two or more of these parameters vary, it is difficult to maximize the cooling rate. Optimization techniques are very handy in such cases, where single or multiple objective functions are to be minimized or maximized depending on multiple factors. In this study, genetic algorithm [GA], a bio-inspired optimization algorithm, is used to optimize the cooling rate of a hot mild steel [MS] plate subject to variation in the air flow rate and the upper and lower nozzle bank distances. The initial temperature of the plate is kept constant, and the plate remains stationary. A laboratory-scale ROT is used to experimentally determine the initial set of data for the GA and then optimize the same for maximization of cooling rate.
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Kumar, R.K., Sinha, S.K., Lahiri, A.K.: Real time simulator for the runout table of hot strip mills. In: Industry Applications Conference, 199, Thirty-First IAS Annual Meeting, IAS’96, Conference Record of the 1996 IEEE, vol. 4, pp. 197–2618 (1996)
2.
Phaniraj, M., Shamasundar, S., Lahiri, A.K.: Relevance of ROT control for hot rolled low carbon steels. Steel Res. Int.
72(5–6), 221–224 (2001)
CrossRef
3.
Bhattacharya, P., Samanta, A.N., Chakraborty, S.: Spray evaporative cooling to achieve ultra-fast cooling in runout table. Int. J. Therm. Sci.
48(9), 1741–1747 (2009)
CrossRef
4.
Mohapatra, S.S., Ravikumar, S.V., Jha, J.M., Singh, A.K., Bhattacharya, C., Pal, S.K., Chakraborty, S.: Ultra-fast cooling of hot steel plate by air atomized spray with salt solution. Heat Mass Transf.
50(5), 587–601 (2014)
CrossRef
5.
Aamir, M., Qiang, L., Xun, Z., Hong, W., Zubair, M.: Ultra fast spray cooling and critical droplet diameter estimation from cooling rate. J. Power Energy Eng.
2(04), 259–270 (2014)
CrossRef
6.
Zhou, C., Ye, Q., Yan, L.: Effect of ultra-fast cooling on microstructure and properties of high strength steel for shipbuilding. In: HSLA Steels 2015, Microalloying 2015 & Offshore Engineering Steels 2015, Springer, Cham, pp. 1179–1185 (2016)
CrossRef
7.
Longhai, L., Qiong, Z.: Analysis on influence factors of the cooling efficiency of moderate thick plates in the quenching with controlled cooling. In: Second International Conference on Computer Modeling and Simulation 2010, vol. 2, pp. 390–393 (2010)
8.
Mishra, P.C., Nayak, S.K., Chaini, R., Ghosh, D.P., Samantaray, B.B.: Effect of controlling parameters on heat transfer during spray impingement cooling of steel plate. Am. J. Eng. Res. (AJER)
2(9), 8–16 (2013)
9.
Visaria, M., Mudawar, I.: Application of two-phase spray cooling for thermal management of electronic devices. IEEE Trans. Compon. Packag. Technol.
32(4), 784–793 (2009)
CrossRef
10.
Sarkar, P., Aditya, A., Ghosh, A., Mandal, P.: Experimental observation of cooling characteristics of hot steel plate in an ROT. J. Basic Appl. Eng. Res.
4(3), 296–298. p-ISSN: 2350-0077; e-ISSN: 2350-0255 (2017)
11.
Mandal, P., Sarkar, B.K., Saha, R., Mookherjee, S., Acharyya, S.K., Sanyal, D.: GA-optimized fuzzy-feedforward-bias control of motion by a rugged electrohydraulic system. IEEE/ASME Trans. Mechatron.
20(4), 1734–1742 (2015)
CrossRef
12.
Singh, J., Chutani, S.: A survey of modern optimization techniques for reinforced concrete structural design. Int. J. Eng. Sci. Invention Res. Dev.
2(1), 55–62 (2015)
13.
Mandal, P., Sarkar, B.K., Saha, R., Mookherjee, S., Sanyal, D.: Designing an optimized model-free controller for improved motion tracking by rugged electrohydraulic system. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng.
230(5), 385–396 (2016)
CrossRef
14.
Eksin, C.: Genetic algorithms for multi-objective optimization in dynamic systems. In: Proceedings of the 26th International System Dynamics Conference (2008)
Herreros, A., Baeyens, E., Perán, J.R.: Design of PID-type controllers using multi-objective genetic algorithms. ISA Trans.
41(4), 457–472 (2002)
CrossRef
17.
Sadegheih, A.: Scheduling problem using genetic algorithm, simulated annealing and the effects of parameter values on GA performance. Appl. Math. Model.
30(2), 147–154 (2006)
CrossRef
18.
Zhai, Y.Y., Li, Y., Ma, B.Y., Yan, C., Jiang, Z.Y.: The optimisation of the secondary cooling water distribution with improved genetic algorithm in continuous casting of steels. Mater. Res. Innovations
19(sup1), 1–26 (2015)
CrossRef
19.
Abdelsalam, Y.O., Alimohammadi, S., Pelletier, Q., Persoons, T.: A multi-objective genetic algorithm optimisation of plate-fin heatsinks. In: 2017 23rd International Workshop on IEEE Thermal Investigations of ICs and Systems (THERMINIC), pp. 1–6 (2017)
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GA Optimization of Cooling Rate of a Heated MS Plate in a Laboratory-Scale ROT
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