High-Performance Prediction of Molten Steel Temperature in Tundish through Gray-Box Model

Abstract

A novel gray-box model is proposed to estimate molten steel temperature in a continuous casting process at a steel making plant by combining a first-principle model and a statistical model. The first-principle model was developed on the basis of computational fluid dynamics (CFD) simulations to simplify the model and to improve estimation accuracy. Since the derived first-principle model was not able to estimate the molten steel temperature in the tundish with sufficient accuracy, statistical models were developed to estimate the estimation errors of the first-principle model through partial least squares (PLS) and random forest (RF). As a result of comparing the three models, i.e., the first-principle model, the PLS-based graybox model, and the RF-based gray-box model, the RF-based gray-box model achieved the best estimation performance. Thus, the molten steel temperature in the tundish can be estimated with accuracy by adding estimates of the first-principle model and those of the statistical RF model. The proposed gray-box model was applied to the real process data and the results demonstrated its advantage over other models.

Modeling of a Thermo-Electromagneto-Hydrodynamic Problem in Continuous Casting Tundish with Channel Type Induction Heating

“…29,30) The temperature distribution is coupled with the velocity field through the buoyancy term in Eq. (11).…”

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

“…Fuzzy Inference System (FIS) was used for the estimation of the error of the WB model of the process. Okura et al [92] used a parallel GB model for the estimation of molten steel temperature in a continuous casting process. Partial least squares (PLS) and random forests (RF) were used as BB models.…”

Prediction of Molten Steel Temperature in Steel Making Process with Uncertainty by Integrating Gray-Box Model and Bootstrap Filter

“…The second sub-model describes the phenomena during the discharging of molten steel from the ladle to the tundish. The first-principle model adopted here is basically the same as the model developed by Okura et al (2013), and it is summarized in the Appendix.…”

“…Thus, to predict and control TD temp, various models such as first-principle models (Austin et al, 1992;Xia and Ahokainen, 2001;Zabadal et al, 2004;Jormalainen and Louhenkilpi, 2006;Belkovskii and Kats, 2009), statistical models (Sonoda et al, 2012), and gray-box models (Gupta and Chandra, 2004;Okura et al, 2013;Ahmad et al, 2014) have been proposed. Although deterministic models are dominant in the literature, a stochastic model is preferable to cope with process uncertainties, such as those in temperature measurements, composition and weight of added alloys, the extent of oxidation reactions for removal of impurities, and degradation of ladles.…”