Conference Proceedings
12th Mill Operators' Conference 2014
Conference Proceedings
12th Mill Operators' Conference 2014
How to Prioritise Process Improvements
Mining companies routinely invest in improvements to their processing operations, but often these enhancements do not work to full effect.Improvements can be ineffectual because underlying process problems are not identified or prioritised for correction or repair.This is frequently because black box' solutions deployed to implement process control improvements typically rely on instrumentation to provide data for algorithms to work effectively, and these solutions are often ineffective or disabled at mineral processing sites.There are three key reasons they can fail to deliver: vital instrumentation is not calibrated or functioning as required basic process control and loop tuning was not implemented correctly or is in need of tuning there is an inability to support the black box' solutions on-site._x000D_
Many of these problems can be overcome by implementing operational analytics that identify where problems are occurring and prioritise where improvements need to be made.Incorporating operational analytics into process improvement is not a new methodology. It is more a back-to-basics approach to make more efficient use of capital and systems already deployed.Data collection systems, such as the OSIsoft PI System, provide real-time and historical process information for process control experts to analyse and identify areas for improvement. However, most systems are not used effectively. With some simple enabling, data collection systems can provide evidence for prioritising investments in improvements and determining an investment's effectiveness.MIPAC advocates that the initial step, with analytical evidence, is that clients review the control strategies and implement small influential instrument repairs/calibrations and operator training. This can significantly boost process productivity without the need to spend large sums of money on new capital or employing expensive, difficult to maintain black box' solutions.A simple Stage-Gate-style approach ensures that small investments in evidence-based process improvements deliver significant benefits to mining operations.CITATION:Hill, D J, 2014. How to prioritise process improvements, in Proceedings 12th AusIMM Mill Operators' Conference 2014, pp 307-314 (The Australasian Institute of Mining and Metallurgy: Melbourne).
Many of these problems can be overcome by implementing operational analytics that identify where problems are occurring and prioritise where improvements need to be made.Incorporating operational analytics into process improvement is not a new methodology. It is more a back-to-basics approach to make more efficient use of capital and systems already deployed.Data collection systems, such as the OSIsoft PI System, provide real-time and historical process information for process control experts to analyse and identify areas for improvement. However, most systems are not used effectively. With some simple enabling, data collection systems can provide evidence for prioritising investments in improvements and determining an investment's effectiveness.MIPAC advocates that the initial step, with analytical evidence, is that clients review the control strategies and implement small influential instrument repairs/calibrations and operator training. This can significantly boost process productivity without the need to spend large sums of money on new capital or employing expensive, difficult to maintain black box' solutions.A simple Stage-Gate-style approach ensures that small investments in evidence-based process improvements deliver significant benefits to mining operations.CITATION:Hill, D J, 2014. How to prioritise process improvements, in Proceedings 12th AusIMM Mill Operators' Conference 2014, pp 307-314 (The Australasian Institute of Mining and Metallurgy: Melbourne).
Contributor(s):
D J Hill
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- Published: 2014
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- Unique ID: P201409034