1. Division of Operation and Maintenance Engineering Wear prediction of grinding mill liners Farzaneh Ahmadzadeh, Jan Lundberg E-mail: Farzaneh.ahmadzadeh@ltu.se Phone: 0920 49 2106 1

2. Presentation overview Purpose of the study Data collection/ generation Methodology Results Conclusions Future research 2

3. Purpose of the study Why prediction of `Liner Wear’ is important? Goal To optimize: mill profitability wear measurement replacement and maintenance scheduling by means of estimation of the wear of the mill liners 3

4. Mill liners (lifter bar) provide the wear-resistant surface within grinding mills and impart motion to the charge. Liner wear is due to abrasive actions and unwanted impact between the charge and the liners which lead to a reduction in the height and volume of the mill liners which affects the trajectory and the shape of the charge and efficiency of the mills. 4

5. Data collection three years shell feed and mid shell part Two sources were used to gather life cycle and condition monitoring data for three years for the shell feed and mid shell part of liners. Metso (Condition monitoring data). Raw data concerning the liner maintenance, inspection and replacement schedule Boliden mine ( Process data ) ore type, ore feed, power, speed, torque, water addition, grinding energy and load 5

6. Data collection from Metso Mineral (condition monitoring data (height and life)) Interpolating methods PCHIP = “Piecewise Cubic Hermite Interpolating Polynomial” Spline Height(mm) Remaining life (%) 6

7. Data preparation for NN 2009-12-222011-04-10 2008-04-15 2009-09-23 2 Cycle condition monitoring data 7

8. Methodology Multiple Regression Analysis Artificial neural network Black Box Yi where, i=1, 2 where, k=1, 2, …n 8

9. Multiple Regression Dependent variable 'X' Intercept Ore type Ore feed PowerSpeedTorqueLoad Grinding energy Water addition R2R2 Coefficients990,634,23-0,090,50-11,81-10,290,11-1,598,89 0,42 P-value0%1%0%3% 1%86%76%0% Dependent variable 'X' Intercept Ore type Ore feed PowerSpeedTorqueLoad Grinding energy Water addition R2R2 Coefficients298,771,44-0,040,17-4,12-3,320,01-0,433,13 0,40 P-value0%2%0%3% 1%98%82%0% Table (2): Height without energy and load Table (1): Remaining life without energy and load 9

10. ‘Remaining Volume' InterceptOre typeOre feedPowerSpeedTorque Water additi on R2R2 Coefficients308,291,42-0,030,18-4,37-3,473,10 0,40 P-value0%2%0%2%1%0% ‘Remaining Height' InterceptOre typeOre feedPowerSpeedTorque Water addition R2R2 Coefficients1039,044,19-0,090,53-12,84-10,848,78 0,42 P-value0%1%0%1% 0% Table (4): ´Remaining h eight without energy and load Table (3): Remaining volume without energy and load Multiple Regression 10

11. Remaining Hight Remaining Volume Ore type Grinding energy Ore feed Power Torque Water addition Load Speed Runing time 25 Nerons 50 Nerons Architecture of the proposed MLP-ANN model Three layer feed-forward back-propagation 11

12. Training and verification The set of all known sample is broken into two independent set. training set : group of samples used to train the neural network. testing sets : group of samples used to test the performance of the data. 886 Training Data 708 Testing data 177 12

13. Learning and generalization Test for learning – Testing the ability to produce outputs for the sets of inputs (seen data) that were used in the training. Test for generalization – Testing the ability to respond to the inputs set of (unseen data) that were not included in the training process. 13

14. 14

15. 15

16. 16

17. Results of the ANN High degree of correlation between the input and output variables The performance of the NN model was highly consistent with training as well as test data The proposed model was able to approximate the input-output function accurately 17

18. Conclusions The life cycle data was analyzed by regression analysis and artificial neural networks. Regression analysis failed to produce acceptable results based on R 2 values. Neural network found to be very effective in defining a function which was capable of establishing good correlation between the input and output variables. 18

19. Future direction Well come any oppurtinity for supporting continuation of this research for: Collecting further sets of life cycle data for testing the accuracy of the network. Integrating this assessment technique for online estimation of the wear of the liners and commercializing it. Applying the proposed method to other components in the mine or other industry. Integrating this assessment technique with some sort of diagnostic tools to identify the faults and deterioration mechanisms right from the early stages of machine life. 19

20. Thank you 20