Prediction of Small Body Weight of Ores in Lingshan Muchongshan Mining Area,Fengyang County,Anhui Province Based on the Grade of Silicon,Aluminum,and Iron in Ores
The small weight of ore is an important parameter in the process of resource reserve estimation,and its accuracy directly affects the economic evaluation of mineral deposits and the results of mine reserve estimation.In order to reduce the error of small weight of ore,under the background of technological innovation in mining technology and the digital era,this paper uses Python language to construct a BP neural network prediction model between the grade of silicon aluminum iron in ore and the small weight of ore,and realizes the prediction of small weight of ore in the mining area.When the mean square error loss value is 0.0022,the prediction result of this model has an accuracy of 96.55%.By comparing the measured values and predicted values,select samples with large deviations for re measurement,verify the small body weight of the samples,and ensure the reliability and accuracy of the small body weight values of each sample.
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