University of Science and Technology Beijing

North China University of Techonlogy

Northeastern University

Yanshan University

Ying Ren

Lifeng Zhang

Haojian Duan

Wei Chen

Yan Luo

Weijian Wang

Dinghan Li

Qiang Ren

Yuexin Zhang

A method and a system for obtaining fluid flow directions of a tundish water model are provided, and the method includes the following steps: building the tundish water model with a preset similarity ratio with a tundish prototype; carrying out an ink diffusion experiment in the tundish water model, and shooting a video of an ink diffusion process; carrying out a binarization processing on the video of the ink diffusion process frame by frame to obtain binarized images; obtaining a time grayscale map based on grayscale values of the binarized images; obtaining a velocity vector map of the tundish water model based on grayscale gradients of the time grayscale map; and obtaining a moving direction of the ink based on the velocity vector map, and obtaining a flowing direction of fluid in the tundish water model based on the moving direction.

Method and system for obtaining fluid flow directions of tundish water model

A method analyzes global average grayscale change of tundish ink tracing experiment. The method includes the following steps: building a water model based on a prototype size and production parameters of a tundish; carrying out experiments by using the water model to obtain a video file of the ink tracing; processing the video file and extracting a characteristic curve of a global average grayscale changing with the time; analyzing the characteristic curve, and extracting a global peak grayscale time, a global tracer residual volume and a global emptying time as indicators for evaluating a tundish structure and flow characteristics of the tundish structure.

Method and system for analyzing global average grayscale change of tundish ink tracing experiment

A method for predicting a yield of calcium in a calcium treatment process based on deep neural network as provided relates to a calcium treatment process of molten steel refining in the field of iron and steel metallurgy, and includes steps of: obtaining production and operation data information of each of charges and thereby constructing a dataset; training and testing a deep neural network based on constructed dataset to establish a prediction model; and based on the prediction model, predicting and calculating current yield of calcium by taking actual production and operation data information of each charge as input. The method can predict the yield of calcium in the calcium treatment process, is favorable for accurately controlling a calcium content of steel, can stably control the calcium treatment process, improve the calcium treatment effect, improve the product quality, and ensure the production stability.

Method for predicting yield of calcium in a calcium treatment process based on deep neural network

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Beijing, China

Ying Ren