Mathematical simulation of hot metal desulfurization during KR process coupled with an unreacted core model

A three-dimensional mathematical model was established to predict the multiphase flow,motion and dispersion of desulfurizer particles,and desulfurization of hot metal during the Kanbara reactor(KR)process.The turbulent kinetic energy-turbulent dissipation rate(k-ε)turbulence model,volume-of-fluid multiphase model,discrete-phase model,and unreacted core model for the reaction between the hot metal and particles were coupled.The measured sulfur content of the hot metal with time during the actual KR process was employed to validate the current mathematical model.The distance from the lowest point of the liquid level to the bottom of the ladle decreased from 3170 to2191 mm when the rotation speed increased from 30 to 110 r/min,which had a great effect on the dispersion of desulfurizer particles.The critical rotation speed for the vortex to reach the upper edge of the stirring impeller was 70 r/min when the immersion depth was 1500 mm.The desulfurization rate increased with the increase in the impeller rotation speed,whereas the influence of the immersion depth was relatively small.Formulas for different rotation parameters on the desulfurization rate constant and turbulent energy dissipation rate were proposed to evaluate the variation in sulfur content over time.

Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM

According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy(LIBS) has shown a great potential for rapid analysis in steel industry.Nevertheless, the accuracy and precision may be limited by complex matrix effect and selfabsorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine(GA-KELM) is proposed for quantitative analysis of multiple elements(Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples.First, the standardized peak intensities of selected spectra lines are used as the input of model.Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer's output matrix. Finally, the least square is applied to calculate the model's output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor(R^2), Root-meansquare Errors of Calibration(RMSEC), Root-mean-square Errors of Prediction(RMSEP) of GAKELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.