Edited by Sharyn Macnamara
The metallurgical processing industry thrives on efficiency and optimisation, writes Dr Nicolaas C Steenkamp.
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Dr Nicolaas C Steenkamp is an independent consultant, specialising in geological, geotechnical and geometallurgical projects and mining project management. He has over two decades of industry experience with global exposure. (ncs.contract@gmail.com) |
Traditionally, steady-state mass and energy balance calculations provided a fundamental framework for process understanding. However, the industry is increasingly embracing the complexities of real-world operations by leveraging digital twins and dynamic simulations.
Digital twins – a mirror image of reality
A digital twin, in the context of metallurgical processing, is a virtual representation of a physical plant or process. It integrates real-time data from sensors, historical data and process models to create a comprehensive digital replica. This replica can be used to simulate process behaviour, predict outcomes and optimise operations in real- time, explains Samantha Edwards, senior process engineer of SKEmet.
Steady-state simulations
The fundamentals of steady-state simulations assume constant operating conditions. It focuses on mass and energy balances for a specific time and condition in the process, neglecting transient behaviour and time variations. As an example, calculating the concentrate recovery and grade from a bank of flotation rougher cells given the flowrate from the upstream process. This assumes a constant feed with consistent composition and operating conditions across the bank.
These simulations have limitations. For instance, steady-state models cannot capture process start-up, shutdowns or disturbances that cause deviations from the assumed constant conditions. They may lead to inaccurate predictions in real-world scenarios with fluctuating conditions.
Dynamic simulations
Dynamic simulations account for time-dependent variations in process variables like feed rate, temperature and pressure. They incorporate kinetic models and physical property correlations to predict the dynamic behaviour of the process over time.
An application example would be simulating the temperature profile and residence time distribution within a roaster furnace, considering changes in feed rate and fluctuations in fuel supply.
The main benefits of dynamic simulations are that they offer a more realistic representation of the process, enabling improved process design, and optimising equipment sizing and control strategies for efficient operation under variable process conditions.
They also offer enhanced control system performance by providing valuable insights for developing advanced control algorithms that adapt to real-time process variations. They further assist in reduced operational costs, through prediction of equipment wear and tear, anticipating maintenance needs, and optimising energy consumption based on dynamic behaviour.
Digital twin and dynamic simulation combination
Digital twins, powered by dynamic simulations, bridge the gap between steady-state models and the complexities of change found in real-world metallurgical processes. Integrating real-time data with dynamic simulations, offers several advantages by transforming a process plant from being reactive to proactive. The controls in the plant can respond quickly and more effectively, resulting in a continually evolving process plant capable of meeting changing conditions and objectives.
Improved fault detection and diagnostics will identify and diagnose issues early by comparing real-time data with simulated behaviour, allowing for proactive maintenance, and reducing downtime.
Utilising the digital twin to train operators on safe and efficient process operation in a simulated environment before encountering real-world scenarios enhances operator training.
Virtual experimentation can be conducted with various “what- if” scenarios without disrupting actual operations, testing new process configurations and evaluating potential impacts before implementation.
Dynamic simulation application
Copper smelting is a very topical example of the practical application for industry, notes Edwards. Simulating dynamic behaviour in a smelting furnace allows for optimisation of feed blends and charging strategies to achieve consistent product quality and reduce energy consumption along with predicting slag formation and tapping schedules to improve furnace efficiency.
In hydrometallurgical processes, dynamic simulations can be used to model the activity of leaching reactors, considering factors like feed composition, temperature and reagent addition to optimise metal extraction. They allow the operator to simulate the behaviour of precipitation circuits to ensure efficient product recovery and minimise environmental impact.
Conclusion
While steady-state mass and energy balance calculations remain valuable for initial process design and understanding, the limitations become evident in real-world scenarios marked by dynamic fluctuations. Digital twins, powered by dynamic simulations, offer a powerful alternative, enabling continuous process optimisation, improved fault detection and enhanced operational decision-making. As the metallurgical processing industry strives for efficiency and sustainability, embracing the dynamic world through digital twins holds immense potential for unlocking new levels of performance and innovation, concludes Edwards.