An Adaptive Artificial Neural Network to Model a Cu/Pb/Zn Flotation Circuit

1. An Adaptive Artificial Neural Network to Model a Cu/Pb/Zn Flotation Circuit Saiedeh Forouzi and John A. Meech University of British Columbia Department of Mining and Mineral Process Engineering

2. Introduction • Massive sulfide deposit of Zn, Pb, Cu and Ag located close to Bathurst in New Brunswick • Ore production began in 1964 with a milling rate of 4500 tpd • More than 80 million tonnes have been processed with about 55 million tonnes remaining • The mill capacity has increased to 10,500 tpd

3. Introduction - continued 9.2% Zn Zn Conc. (52.5%) Pb Conc. (40%) 3.6% Pb Process 0.36% Cu 105 g/t Ag Cu Conc. (23.5%) Bulk Conc. (39% Zn + 19% Pb) Tailing

4. Concentrator Flow Sheet Crushing Plant Lines1&2 Fine Ore Bins Lines3 Fine Ore Bins Line 1&2 Grinding Line 3 Grinding L3 CuPb L1&2 CuPb L3 Zn L1&2 Zn Cu Sep. Pb Upgrd Bulk Float Tailings Pb Conc. Cu Conc. Bulk Conc. Zn Conc.

5. Project Background • process inputs include tonnage rate, water addition, particle size distribution, reagents, flotation cell levels • process outputs are Cu/Pb/Zn grades of concentrates and tailing • process setpoints for final & intermediate variables are not fixed because of - variable head grades - changable smelter contracts

6. Project Background (continued) • instrumentation - XRF on-stream analyser - particle size monitor - tonnage weigh scale - pulp flowmeters - pulp density gauges - reagent flowmeters - pH meters - thermocouples - flotation cell levels - air flowmeters • control systems - “grind” control - tonnage control - cell level control - reagent control

7. Project Objectives & Scope Long Term • use smelter contracts and headgrades to find best achievable product grades and establish setpoints for control variables Short Term • establish an Artificial Neural Network Model to predict product assays for Line 3 Cu/Pb circuit to provide proof-of-concept of the model

8. General Concentrator Flow Sheet Crushing Plant Lines1&2 Fine Ore Bins Lines3 Fine Ore Bins Line 1&2 Grinding Line 3 Grinding L3 CuPb L1&2 CuPb L3 Zn L1&2 Zn Cu Sep. Pb Upgrd Bulk Float Tailings Pb Conc. Cu Conc. Bulk Conc. Zn Conc.

9. Cu/Pb Flotation Circuit - Line 3 Aeration 2nd Clnr 1st Clnr Roughers From Grinding Cu-Pb To Zn Conc. Circuit Regrind Mill

10. Methodology • Develop an ANN model to represent the relationships between the I/O variables • Update the model as required to reflect relationship changes and maintain accuracy and robustness • Develop a fuzzy algorithm to decide when to adapt the model

11. Tools • G2, from Gensym, a software tool for intelligent real-time system development • GDA and NeurOnline are related tools • provide direct access to real-time data • object-oriented software and user friendly • available for use at Brunswick • graphical output and configuration features

12. Artificial Neural Networks • Inspired from neuronal structure of the human brain • Learning and Recall processes in neuron cell connections • Network consists of several layers of processing element X1 n Wj1 Sj =  WjiXi X2 Wj2 Yj . i=1 Sj f(Ij) . . Yj = f(Sj) =f( WjiXi) Wjn Xn

13. ANN - activation function Sigmoid function - most popular method - ouput signal scaled between 0 and 1 - a convenient differentiable form f '(Sj) = ej (1- ej ) -Sj f(Sj)= 1/ (1+ e )

14. ANN Model Architecture • Input, output and hidden layers Wji Wkj X1 n Hj = f (Ij) = f (  WjiXi) i=1 X2 Y1 1j  m . . . . . . . . . m k=1...p i=1...n j=1...m Yk = f (Ik) = f (  WkjHj) Xn Yp j=1 1k  p Bias _ p Input Hidden Output E = 1/p  ( Yk - Yk) k =1

15. ANN Models - features Advantages: • generates knowledge by learning from actual data • able to perform massive parallel processing • able to model very complex nonlinear problems Drawbacks: • lots of data may be required • data collection plays a vital role • Learning can be slow for large networks

16. ANN Models - issues • quality of data and reliability of instrumentation • separation of data into training and testing sets • lots of data may be required • data preprocessing (filtration, synchronization) is vital • scaling of all data is essential • Learning can be slow for large networks • stability of process relationships • design issues (bias, hidden nodes, algorithm, learning rate) • separate networks for predictive models • inclusive networks for classification models

17. ANN Models at BMS • Number of networks (12 separate models) • Variables: actual values and changes in value Inputs (58) Output Values of control and load variables Model Change in Assay Changes in control and load variables Actual Assay

18. Data Pre-processing for ANN Model • Phase lags determined by mass flow and process capacity Input Output Time

19. Data Pre-processing for ANN Model • Phase lags as a function of mass flow • All data are scaled between 0 and 1

20. ANN Model at BMS • Phase lags as a function of mass flow • All data are scaled between 0 and 1 • Use sigmoid function in all layers • Data set size (~1300 records) • Random data separation for training and testing • Error calculation on both training and testing data

21. Adaptive ANN Model • process relationships are never fixed • How often is retraining required? • Which data to use for retraining? • establish an intelligent algorithm to answer these questions

22. Process Adaptive Model at BMS New Model Predicted Output ANN Model - Error Inputs + Actual Output Retraining Algorithm Data file Updating Data Validation

23. Adaptive Model at BMS Current Data at t(adjusted phase lags) Actual Inputs at t Changes in Inputs from t-1 Actual Assay at t-1 Predicted Change in the Assay at t ANN Model Predicted Assay at t Actual Assay at t-1 Actual Assay at t Predefined Threshold > Error & Cumulative Error Actual Change in Assay at t Predicted Change in Assay at t

24. Adaptive Model at BMS Case One - New Data Set Output Inputs Current Data Max Old Data Min

25. Adaptive Model at BMS Case One - New Data Set Output Inputs Max Data set for retraining Min

26. Adaptive Model at BMS Case Two - Similar Data Set Output Inputs Current Data Max Old Data Min

27. Adaptive Model at BMS Case Two - Similar Data Set Output Inputs Max Data set for retraining Min

28. Adaptive Model at BMS Case Three - Similar input/different output Output Inputs Current Data Max Old Data Min

29. Adaptive Model at BMS Case Three - Similar input/different output Output Inputs Max Data set for retraining Min

30. Adaptive Model at BMS Case Four - New data set but data file is full Output Inputs Current Data Max Old Data Min

31. Adaptive Model at BMS Case Four - New data set but data set is full Output Inputs Max Data set for retraining Min

32. When to Retrain? • The error in the model • The amount of new data High Error Low Error No retraining High Low Retraining % of New Data

33. Process Compensating Feed Forward Model-Based Control New Setpoints Predicted Output Knowledge Base ANN Model Inputs Actual Output

34. Compensating Feed Forward Model-Based Control • Knowledge about the control variables and their relationships • Using the knowledge of experts • Setting rules to change setpoints of control variables to obtain desired output • Using the model to test established rules

35. Implementation Schedule Description Oct Dec Nov Organizing Data Filtering and Phase lags Model Training and Testing ANN Model Setting Up On-Line Data Collection Creating and Testing On-Line Adaptive System

36. Advantages • A self-adaptive model which represents the current process • Having a model to predict process outputs under any condition • Accounting for economics in establishing setpoint to achieve higher efficiencies • Better process control by increased flexibility

37. Results • Original model gave the following errors: RMSE = 0.107 for Training Data RMSE = 0.181 for Testing Data • Retraining began after less than 8 hours • New model gave RMSE = 0.103 for Training Data RMSE = 0.443 for Testing Data • After 1 month, RMSE settled into the range of 0.1-0.2 and retraining periods were ~ 7 days

38. Potential Problems • Data should be derived from testwork • Once supervisory control is implemented, the process relationships will be masked • Instrumentation reliability issues exist • Real-time issues with 12 ANN models running in parallel • Identification of "optimum" set points

39. Conclusion • ANN modeling of plant data with G2 is feasible and practical • Adaptation of the model can occur in real-time • Model-based supervisory control can now be tested and implemented • ANN can be used to identify the important control variables in the process

40. Recommendations • Application of the model to the remaining circuit assays should proceed • Development of the supervisory control knowledge base should proceed • Database updating should be done using forced variations in the process to ensure the discovered relationships are valid