METHOD FOR PREDICTING YIELD OF CALCIUM IN A CALCIUM TREATMENT PROCESS BASED ON DEEP NEURAL NETWORK

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.