Optimal prediction model on sedimentation parameters of pre-magnetized crude tailings slurry
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Abstract:
In order to improve the dewatering and concentrating effect of crude tailings slurry (CTR), magnetic treatment technique was introduced into dewatering and concentrating of CTR, and a GA-SVM(genetic algorithm-support velocity machine) model was established to optimize sedimentation parameters of CTR. The SVM model for predicting sedimentation parameters of pre-magnetized CTR was established and trained with sample data got from orthogonal experiments, taking magnetic induction, magnetized time, cycling velocity and mass concentration of CTR, unit consumption of flocculant as input factors, and sedimentation velocity as comprehensive output factor. Then a GA-SVM model for optimizing sedimentation parameters of pre-magnetized CTR could be obtained after parameters of SVM model optimized by the genetic algorithm. Furthermore, the GA-SVM model was adopted into an iron mine's pre-magnetized CTR to optimize its sedimentation parameters, and the optimized sedimentation velocity was about 155 cm/h when magnetic induction, magnetized time, cycling velocity of CTR and unit consumption of PAC flocculant was 0.192 T, 1.85 min, 1.92 m/s and 28 g/t, respectively. The study results show that dewatering and concentrating effect of CTR could be improved and PAC could be saved by 40% under suitable condition of magnetic treatment. For optimizing sedimentation parameters of pre-magnetized CTR, the relative prediction error of GA-SVM model was less than 5%, which suggests that the GA-SVM model has higher prediction precision. The study also providing a new method to CTR's dewatering and concentration as well as its parameter optimization.
