Prediction model of floatation recovery ratio for a gold mine

As an important production index in the present gold-mine beneficiation process, floatation recovery ratio is mainly ob-tained by laboratory test, which has long cycle time and is hard for the staff to control the flotation process standard. Based on massive actual production data, two prediction models of floatation recovery ratio for a gold mine were established respectively by using multiple linear regression and BP neural network method. By analyzing the predictive errors of the two models, it is approved that the prediction model based on BP neural networks can provide a better accuracy. When the relative prediction errors are within ±3%, the prediction accuracy reaches 91%, thus applying a good reference for practical production.