Online forecasting model of tundish nozzle clogging

A nozzle clogging online forecasting model based on hydrodynamics engineering was developed, in which the actual flow rate was calculated from the mold width, thickness, and casting speed. There is a linear relationship between the theoretical flow rate and the slide gate opening ratio as the molten steel level, argon flow rate, and the top slag weight are kept constant, and the relationship can be obtained by regression of the data collected at the beginning of the first heat in each casting sequence when the nozzle clogging does not occur. Then, during the casting, the theoretical flow rate can be calculated at intervals of one second. Comparing the theoretical flow rate with the actual flow rate, the online nozzle clogging ratio can be obtained at intervals of one second. The computer model based on the conception of the nozzle clogging ratio can display the degree of the nozzle clogging intuitively.

Short-term traffic flow online forecasting based on kernel adaptive filter

Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.