Increasing Grinding Circuit Robustness with Advanced Process Control

Transcription

1 Increasing Grinding Circuit Robustness with Advanced Process Control A.Rantala, P.Blanz, K.Aberkrom, O.Haavisto MetPlant 2015, 7-8 September, Perth

2 Outline Introduction Basic control of grinding Key measurements Advanced process control Objectives of grinding optimisation Case study examples On-line mill charge measurement Conclusions

3 Introduction Grinding is often a bottleneck for plant capacity Optimization Maximize profit High running and energy costs Grinding affects the performance of downstream processing MES Advanced Process Control Process Control Control maintenance and production processes Schedule resources Reporting and transparency Manipulate base level targets Process models, Soft sensors Multivariable control Model based control PID control Ratio control Sequences How to process more compex and lower head grade ores more efficiently? Instrumentation Process and Process Equipment Actuators (valves, pumps) Measurements Analyzers (XRF-analysis, Image analysis, Particle size measurement) Crushing Grinding Flotation Dewatering

4 Basic control of a grinding circuit

5 Key measurements

6 Advanced Process Control (APC) Higher level control based on multiple inputs, process knowledge and multiple outputs (set-points) Expert control Rule based control, easy to understand Model predictive control (MPC) Uses process models to find optimal control actions to reach targets Usabilitity is a key element (often forgotten) to gain operator trust and high utilization rates Potential for significant continuous improvement

7 Objectives Maximize grinding capacity while keeping the particle size between optimal range Minimize disturbances and stabilize feed to downstream processing Optimize circulating loads, manage variable interaction and delays Minimize energy and consumable usage Increase the availability of the grinding circuit and equipment F80 14 mm F80 14 mm Throughput t/h Work index kwh/t

8 Modular APC template for grinding optimisation Basic framework for an APC application for a grinding circuit Process estimation module ensures the measurement validity and soft sensor and sensor fusion data Cyclone and particle size control modules typically stabilising and limiting Mill control often the master controller pushing the circuit within limits Operators to provide hard limits and targets* *or plant optimiser

9 Outline Introduction Basic control of grinding Key measurements Advanced process control Objectives of grinding optimisation Case study examples On-line mill charge measurement Conclusions

10 Case study 1: A SABC circuit Highly alternating ore hardness Unexpected ore feed shutdowns and spillages Particle size variance Proper particle size distribution important for the downstream processing Good level of instrumentation and basic control Load cells in the mill Particle size analyser APC control strategy was implemented based on the template

11 Case study 1: Results of the on/off trial campaign Result Standard deviation t-test (p) SAG Feed (t/h) % % < Particle Size (% -200 mesh) Process value deviation to target (P80: 80%) % % < SAG Power (kw) % % < Cyclone Feed Density (%) % % < Cyclone Feed Flow (m 3 /h) % % < Pump Box Level (%) % % < 0.005

12 The effect of APC during softer ore feed

13 The effect of APC during harder ore feed

14 Case Study 2: an AG/Pebble mill circuit Low grade feed with alternating hardness Stability and dry flotation feed primary objectives Proper combined particle size to flotation Balancing AG/Pebble mill circulations Allow operators focus on overall efficiency of the plant High level of instrumentation and basic control ensure good basic process stability APC control strategy was implemented based on the template

15 Case Study 2: Results of mill control module Result Standard deviation t-test (p) Flotation dry feed (t/h) % % 0.04 Grinding circuit feed (t/h) % % 0.86* AG Energy/ton milled (kw/t) % % 0.58* Particle size (% -75 µm), Mill 1 Process value deviation from target (75 %) + 5 % % 0.68* Particle size (% -75 µm), Mill 2 Process value deviation from target (75 %) % % < *not significant

16 Case Study 3: On-line mill charge analysis Valuable control input for indicating the state and condition changes of a mill Liner damage Reduced impact area Speed (% of critical) Optimum Charge volume (%) Liner damage Reduced impact area Inefficient impacts Charge volume has a great impact on the grinding efficiency Estimation methods: Mass indicators, e.g. bearing pressure, load cells Noise, aqoustic sensors Power signal Embedded probes, e.g. strain gauge Model-based (soft sensors)

17 On-line mill charge measurement development Direct measurement of toe position with a strain gauge on the mill shell Wireless data transfer and inductive powering* Prototype testing on-going at three AG/SAG mills with rubber liners, a steel lined mill test to commence soon *patents pending

18 Correlation between mill variables and mill charge 6% mill charge variation Mill feed and mill charge (AG mill)

19 Conclusions Lower grade, more complex and changing ore feed increases the challenge of efficient grinding circuit operation APC can provide significant annual benefits through improved stability and more continuous operation at the circuit s limits Adequate level of instrumentation and tuned basic process control is a requirement for APC performance The potential APC benefits demonstrated by two case studies indicate better control of particle size, improved throughput and energy efficiency On-line mill charge measurement development adds an essential variable to grinding circuit control

20 Acknowledgements Companies of the case studies are greatly acknowledged for their co-operation and permitting the data in this paper to be presented

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