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Mine your big data to optimise plant performance
Gaining a complete picture of your plant’s performance can be incredibly challenging. Across all your assets, thousands of sensors are continually taking measurements, monitoring for wear and tear, evaluating inputs and outputs, and more. All this data can be invaluable in helping plant operators identify areas for improvement, but it’s worth nothing if it’s locked in siloes or inflexible systems.
There is a huge opportunity in metallurgical processing plants to unlock the true value of data. If decision-makers can gain a clear picture of plant performance, they can more accurately control for productivity, safety, and profit margins — while also helping reduce waste and meet sustainability targets. To deliver these critical insights, industry leaders are embracing the very latest machine learning technologies and process optimisation solutions.
| Traditional ways of interpreting plant data | With MI Process Optimiser® |
|---|---|
| Subjective – relies on people’s experience | Objective – automatically learns patterns from historical data held in MI Core® |
| Linear – difficult to identify relationships between data components | Non-linear – enables multi-dimensional models |
| Static – rough estimation only for time dependencies | Dynamic – conditional probability estimation of time dependencies |
| Local – looks at each stage of processing independently from one another | Global – considers each step of the process and its relationship to one another, as well as the context of the entire process as a whole |
MI Process Optimiser® pairs the power of machine learning with metallurgical processing statistical analysis to continuously review your plant’s performance — ultimately helping you make smarter decisions to minimise potential losses and maximise profits.
By capturing and centralising data from millions of points across your plant, MI Process Optimiser® can identify powerful patterns in your data and help you land on optimum processing conditions according to a range of different parameters. Using a digital twin, you can compare these conditions with real plant data, investigate issues, and make adjustments in near real-time to optimise production.
MI Process Optimiser® uses millions of data inputs and outputs generated by the digital twin solver from MI Core® to calculate optimum values for controllable variables — ultimately delivering extremely detailed and accurate results.
increased recovery
of potential improvements pa
