Parameter analysis model of ore product based on BP neural network

As China's heavy industry has entered the stage of high-quality development, the efficient utilization of ore and low-carbon treatment is particularly important, in order to find a more efficient ore treatment scheme.Based on the completion of data preprocessing, select constant temperature processing range, based on neural network algorithm, and through the analysis of the existing ore processing cases, using neural network algorithm for learning, analysis, summarized the mathematical model according to the existing ore parameters, adjust the appropriate processing temperature to obtain the qualified rate of as much as possible, test parameter prediction can be used for the product successfully, Modify the input and output parameters of the neural network algorithm.The neural network learning and testing are carried out again, and the processing temperature can be deduced after the testing is successful. In this paper, a parameter analysis model of ore products based on BP neural network is proposed, which can provide a better solution to the prediction problems related to raw ore parameters and processing problems