Optimization of sintering machine parameters using simulated annealing metaheuristic
Karina Assini Andreatta; Flávio Tulio Busatto
Abstract
The sintering process consists of agglomerating fine iron ore with other materials and additives to form a porous agglomerate called sinter. The sinter is used as feedstock for blast furnaces, where it is converted into pig iron, which is the basis for steel production. One of the major challenges of the sintering process is the degradation of the chemical quality of the iron ores, which can affect the quality of the sinter produced. Therefore, this work proposes an approach to optimize the parameters for sintering machines. This approach uses machine learning and computational optimization techniques based on the simulated annealing algorithm by analyzing production history data with the aim of maximizing sinter productivity and yield while ensuring that product quality requirements are met. As a result, the quantity of pellets used in pig iron production was reduced by replacing part of this material with additional sinter produced according to the recommendations of the mathematical model for the sintering machine parameters.
Keywords
References
1 Miller RA. Sintering theory and practice. New York: Wiley; 2010.
2 Shao H, Chen C, Liu H, Liu J, Wang X. Application of artificial neural networks for prediction of sinter quality based on process parameters control. Transactions of the Institute of Measurement and Control. 2020;42(3):422-429. http://doi.org/10.1177/0142331219883501.
3 Goswami A, Biswal AK, Selvan VT, Khan S. Improvement in sinter productivity with intelligent mathematical model based control system. International Journal of Advanced Research in Computer and Communication Engineering. 2015;4(10):555-558. http://doi.org/10.17148/IJARCCE.2015.410125.
4 Li F, He J, Zhou M, Fang B. VLSs: a local search algorithm for distributed constraint optimization problems. International Journal of Pattern Recognition and Artificial Intelligence. 2022;36(3):2259006. http://doi.org/10.1142/ S0218001422590066.
5 Kirkpatrick S, Gelatt CD Jr, Vecchi MP. Optimization by simulated annealing. Science. 1983;220(4598):671-680. http://doi.org/10.1126/science.220.4598.671.
6 Spearman C. The proof and measurement of association between two things. The American Journal of Psychology. 1904;15(1):72-101. http://doi.org/10.2307/1412159.
7 Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics. 2004;69(6):066138. http://doi.org/10.1103/PhysRevE.69.066138.
8 Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics (Oxford, England). 2010;26(10):1340-1347. http://doi.org/10.1093/bioinformatics/btq134.
9 Breiman L, Friedman J, Olshen RA, Stone CJ. Classification and regression trees. New York: Chapman and Hall/ CRC; 1984. http://doi.org/10.1201/9781315139470.
10 Majumder A, Biswas C, Dhar S, Das G. Optimization of process parameters to achieve better mechanical properties and higher productivity in sinter plant. Journal of Materials Engineering and Performance. 2021;30(3):1846-1857.
11 Kato S, Oya K, Higuchi T, Hayasaka Y, Hashimoto K. Improvement of sinter productivity and quality. In: 42º Seminário de Redução de Minério de Ferro e Matérias-primas/13º Seminário Brasileiro de Minério de Ferro / 6º Congresso Internacional de Ciência e Tecnologia da Siderurgia, Rio de Jabeiro, 2012. https://doi.org/10.5151/2594 -357X-22200.
Submitted date:
09/04/2023
Accepted date:
07/01/2024
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email