Abstract
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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Aditya, A., Sarkar, P., Mandal, P. (2019). GA Optimization of Cooling Rate of a Heated MS Plate in a Laboratory-Scale ROT. In: Sahoo, P., Davim, J. (eds) Advances in Materials, Mechanical and Industrial Engineering. INCOM 2018. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-96968-8_30
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DOI: https://doi.org/10.1007/978-3-319-96968-8_30
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