Comparison of data-driven prediction methods for comprehensive coke ratio of blast furnace

نویسندگان

چکیده

Abstract The emission of blast furnace (BF) exhaust gas has been criticized by society. It is momentous to quickly predict the comprehensive coke ratio (CCR) BF, because CCR one important indicators for evaluating emissions, energy consumption, and production stability, also affects composite economic benefits. In this article, 13 data-driven prediction techniques, including six conventional seven ensemble methods, are applied CCR. result ten-fold cross-validation indicates that multiple linear regression (MLR) support vector (SVR) based on radial basis function superior other methods. mean absolute error, root square coefficient determination ( R 2 ) MLR model 1.079 kg·t −1 , 1.668, 0.973, respectively. three SVR 1.158 1.878, 0.975, Furthermore, AdaBoost strong ability generalization performance. methods have significances both in theory practice predicting Moreover, models constructed here can provide valuable hints into realizing control BF process.

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ژورنال

عنوان ژورنال: High Temperature Materials and Processes

سال: 2023

ISSN: ['0334-6455', '2191-0324']

DOI: https://doi.org/10.1515/htmp-2022-0261